How to Find Products by Image Using Visual Search

A shopper sees a backpack they like in a photo, not a product name. Typing “green backpack with a rabbit on it” into a search bar rarely finds it. Visual search does: upload the photo, and the engine returns the closest matches from the catalog directly.

That gap between what people can describe and what they can show is exactly what visual search closes. Here’s how it works, where it earns its place in an online store, and what to check before you evaluate a vendor.

What Is Visual Search in eCommerce?

Visual search is a new way of searching for products on a website using images instead of text.

Rather than typing keywords into a search bar, users can upload an image directly into your site’s search engine. In other words, if a shopper is asking “how do I search with a photo?”, visual search provides the perfect solution.

With just a single image, users can perform the same search they would with text, sometimes with even more accurate results. In the video below, we’ll explain it in more detail.

Visual Search vs. a Standard Text Search

Visual search allows users to:

Take a photo at the moment they start their search
Upload an image already saved on their device

Once the image is uploaded, the search engine instantly begins analyzing it. This makes visual search a practical and intuitive functionality, especially designed for mobile devices, where typing detailed queries can be slow or inconvenient.

You might be familiar with Google’s Visual Search. They allow you to upload a photo, so you can find visually similar images.

How It Differs From Google Lens, Pinterest Lens, and Amazon StyleSnap

Google Lens, Pinterest Lens, and Amazon StyleSnap are consumer tools built to search the open web or a single retailer’s catalog from outside the store. Ecommerce visual search is different in scope: it runs inside a specific store’s search engine and matches only against that store’s own inventory.

The distinction matters for shoppers and for merchants. A shopper using Google Lens might see a product from a competitor’s site. A shopper using a store’s own visual search only ever sees what that store actually sells and can fulfill. For the merchant, that also means the matching logic can be tuned to the catalog: stock levels, variants, and merchandising rules all stay in play, which a general-purpose tool like Lens has no visibility into.

How the Technology Actually Works

Visual search isn’t a single trick. It’s a pipeline: an image goes in, and a ranked list of catalog products comes out.

Image Embeddings and Similarity Matching

At a high level, the system converts the uploaded image into a numerical representation, an embedding, that captures its visual features: shape, color palette, texture, silhouette. It then compares that embedding against embeddings generated for every product image already indexed in the catalog, and ranks results by similarity.

From Upload to Catalog Match: The Flow

The shopper takes a photo or uploads an existing image.
The search engine processes the image and generates its embedding.
That embedding is compared against the indexed catalog in real time.
The closest matches are returned, ranked the same way a text search would rank results, by relevance to what the shopper is looking for.

The step that determines whether this works well in practice is the third one. A catalog with clean, consistent product photography returns tighter matches than one with inconsistent angles, backgrounds, or lighting.

Visual Search Use Cases by Industry

Visual search isn’t equally useful everywhere. It earns its keep in categories where products are hard to describe precisely but easy to recognize on sight.

Fashion. A shopper photographs an outfit they saw someone wearing, or a garment from a magazine, and finds the closest match or similar pieces in the store. Color, pattern, and silhouette carry more of the identifying information than any product name would.

Home decor. A shopper photographs a room, or a single piece of furniture they like, and the store surfaces matching or complementary items. This is where visual search does double duty: it works as a search tool and as a discovery tool, since results often include pieces the shopper wasn’t specifically looking for but that fit the same style.

Automotive parts. A shopper photographs a worn or broken part and searches for the replacement, without needing to know the manufacturer code or exact part name. This is arguably where visual search removes the most friction, because the alternative, describing a part in words or hunting through a parts diagram, is genuinely difficult for a non-specialist.

Does Visual Search Improve Conversion Rates?

The direct evidence is still thin industry-wide, but the logic holds up: visual search removes a step where shoppers commonly give up, the point where they can’t find the right words for what they want. Fewer zero-result searches and fewer abandoned queries should translate into more completed searches that end in a click, which is where conversion starts.

How Doofinder’s Visual Search Works

Doofinder’s visual search analyzes the shape, colors, and visual details of an uploaded photo and compares them against the store’s product catalog to surface the closest matches, at the same speed as a standard text search.

Here’s what that looks like in a live store. A shopper on EurekaKids, a children’s products retailer, uploads a photo of a backpack. Doofinder’s visual search returns the closest matches from the catalog, complete with filters for price, category, and brand, in the same time a text search would take.

Doofinder visual search results for backpacks on EurekaKids online store.

A shopper on EurekaKids uploads a photo of a backpack. Doofinder’s visual search returns the closest matches from the catalog in the same time a text search would take.

The result the shopper sees isn’t different in kind from a normal results page. What’s different is what triggered it: a photo, not a typed query, arriving at the same relevant products.

You can see the full feature set on the Doofinder Visual Search product page, including setup and how it fits with the rest of your store’s search and merchandising.

Powerful Results Without Slowing Down Your Store

One major advantage of visual search is that it maintains the same loading speed as a traditional written search.

Despite the advanced technology working behind the scenes, product results appear just as quickly. This ensures a smooth user experience while helping you improve search engine results within your eCommerce store.

Fast searches mean satisfied users—and satisfied users are more likely to convert.

Visual Search FAQ

How does visual search work?

The shopper uploads or takes a photo, and the search engine converts it into a numerical representation of its visual features, shape, color, pattern, texture. That representation gets compared against every product image already indexed in the catalog, ranked by visual similarity. The closest matches come back the same way a text search would return results, just triggered by an image instead of a query.

What is visual search in eCommerce?

It’s a way for shoppers to search a store’s catalog using an image instead of typed keywords. It matters most when a product is easy to recognize on sight but hard to describe precisely in words, a specific shade of green, an unusual pattern, a particular silhouette. Instead of guessing at the right search terms, the shopper shows the engine what they mean.

How do I add visual search to my online store?

With Doofinder, visual search is part of the search engine setup and doesn’t require custom development.

Does visual search improve conversion rates?

The underlying logic is straightforward: shoppers who can’t find the right words for a product often abandon the search entirely, and visual search removes that failure point. Fewer zero-result searches should mean more shoppers reaching a product page instead of leaving.