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An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity, e.g. a book, with a search engine is that the entity is retrievable, i.e. there are queries for which the system will surface such entity in the top results. However, machine-learned search engines have a high retrievability bias, where the majority of the queries return the same entities. This happens partly due to the predominance of narrow intent queries, where users create queries using the title of an already known entity, e.g. in book search “harry potter”. The amount of broad queries where users want to discover new entities, e.g. in music search “chill lyrical electronica with an atmospheric feeling to it”, and have a higher tolerance to what they might find, is small in comparison. We focus here on two factors that have a negative impact on the …