Just yesterday I saw a talk from Spotify where they presented their own fast retrieval mechanism "Parallel Fusion Router" [1]. It's also in the "learned retrieval" paradigm - in their case they use a teacher LLM to train a much smaller router LLM how to retrieve, using an LLM-as-a-judge based reward function.

From my own experience, these learned methods from Apple, Spotify and others, are highly relevant to hyperscale only - when you are dealing with hundreds of millions of items to choose from (or more), and require super high relevance in the top-5. For all other use cases (up to a few million items) I found that just building a BM25 index, and giving the LLM a BM25 "search tool" and casting a "wide net" wins every single time compared to learned methods (though the latter are more publishable in recsys and icml).

[1] https://research.atspotify.com/2025/9/you-say-search-i-say-r...