I wonder at what point it will be ~as much overhead to pass through a subset of the data with a small yet capable and fast LLM vs. using a crude dot product when doing retrieval

I think a combination works quite well: first getting a small set of candidates from all the data using a lightweight model, and the using a heavy-duty model to rerank the results and get the final candidates.