  Active search is a learning paradigm where we seek to identify as many
members of a rare, valuable class as possible given a labeling budget. Previous
work on active search has assumed access to a faithful (and expensive) oracle
reporting experimental results. However, some settings offer access to cheaper
surrogates such as computational simulation that may aid in the search. We
propose a model of multifidelity active search, as well as a novel,
computationally efficient policy for this setting that is motivated by
state-of-the-art classical policies. Our policy is nonmyopic and budget aware,
allowing for a dynamic tradeoff between exploration and exploitation. We
evaluate the performance of our solution on real-world datasets and demonstrate
significantly better performance than natural benchmarks.
