You can look at https://same.energy/ for an ex-OAer showing what CLIP-like NNs can do for image search.
It's impressive but if Google Images doesn't do it yet, it's almost certainly more to do with the realities of corporate policy, and scaling up & deploying it at GI scale than as a small toy demo on a static set of a few hundred million (as opposed to countless trillions of adversarially changing images). It's certainly not for lack of good NNs. Remember, Google has much better NNs than CLIP already! ALIGN was over half a year ago, and they have MUM and "Pathways" already superseding that.
As always, "the future is already here, it's just unevenly distributed"; it's just practical realities about lag - same way that the RNN machine translation models were massively better for years before Google Translate could afford to switch away from its old n-grams approach, or the speech transcription NNs were great long before they ever showed up on Google YouTube auto-captions, or BERT was kicking ass in NLP long before it became a major signal in Google Search, etc.
Not only does it produce really good results, the interface with it's ability to start another search with either the description or image of any of the results is great for complex searches.
That's interesting indeed!
Note that the description search as well as the image search are both using a knn index on embeddings and not exact search. That helps for finding semantically close by items but indeed for exact reference match it might not be the best solution.
Re-indexing the dataset with something like elastic search would give the reference search results you expect.
