What Is Multimodal Search?

How Multimodal Search Works

Traditional search takes one kind of input: you type words. Multimodal search takes several at once — an image and text, or a photo and voice — and reads them as a single intent. The everyday example is pointing your phone at an object you cannot name and asking a question about it. Instead of guessing keywords, you supply the picture and let a few words do the refining.

Google Lens turned this into a mainstream feature with multisearch, which the company calls “an entirely new way to search with images and text at the same time.” You photograph or screenshot something, then add a phrase — a color, a brand, a related item — and the engine interprets the combined query rather than running two separate searches. Newer versions fold this into conversational search: in Google AI Mode, Lens pairs with a custom version of Gemini so you can ask complex, multi-turn questions about whatever the camera sees.

Because the query begins with an image, the signals that decide what surfaces shift. Visual recognition identifies the object, but the engine then matches it against text it can read — which is why machine-readable content like descriptive alt text, captions, and structured data becomes the deciding factor. This makes multimodal optimization a real part of generative engine optimization for any site with products or visual inventory.

Example of Multimodal Search

Google’s launch of multisearch is the clearest documented case. Announced on April 7, 2022, and delivered through Google Lens, it let users do something new: search with a picture and words simultaneously. Google’s own examples were concrete — screenshot “a stylish orange dress and add the query ‘green’ to find it in another color,” or “snap a photo of your dining set and add the query ‘coffee table’ to find a matching table.” The image supplies what is hard to describe; the text supplies the refinement.

Follow that second example through. A shopper photographs a wooden dining set and types “coffee table.” A plain image search would return more photos of dining sets. Multisearch instead fuses the two inputs — this visual style plus this product category — and surfaces coffee tables that match the wood tone and design. The retailers that appear are the ones whose product pages carry clear images, descriptive labels, and structured product data the engine can read. A gorgeous but unlabeled photo of the perfect matching table never gets matched, because nothing on the page tells the engine what it is.

The takeaway is that multimodal search widens the front door — people can now start a search from something they can see but not name — while keeping the same requirement on the back end: content an engine can read and match. The camera is the input; well-described, well-structured pages are still what win the result.

Frequently Asked Questions

The Bottom Line

Multimodal search lets a single query mix formats — a photo and a phrase, a screenshot and a spoken question — so people can search for things they can see but not easily name. The engine fuses the inputs into one intent, but it still matches that intent against readable signals, which keeps clearly-labeled, well-described content central even when the search begins with a camera.