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Active search is a setting in adaptive experimental design where we aim touncover members of rare, valuable class(es) subject to a budget constraint. Animportant consideration in this problem is diversity among the discoveredtargets -- in many applications, diverse discoveries offer more insight and maybe preferable in downstream tasks. However, most existing active searchpolicies either assume that all targets belong to a common positive class orencourage diversity via simple heuristics. We present a novel formulation ofactive search with multiple target classes, characterized by a utility functionchosen from a flexible family whose members encourage diversity via adiminishing returns mechanism. We then study this problem under the Bayesianlens and prove a hardness result for approximating the optimal policy forarbitrary positive, increasing, and concave utility functions. Finally, wedesign an efficient, nonmyopic approximation to the optimal policy for thisclass of utilities and demonstrate its superior empirical performance in avariety of settings, including drug discovery.

Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery