Understanding and Improving Unique Hit Scaling in Protein Binder Design

Generative models are accelerating the discovery of proteins that bind specific molecular targets, creating new opportunities for therapeutic development. The goal of an in silico campaign is to propose a diverse library of promising candidates for experimental testing, motivating the objective of maximizing unique hits under a fixed compute budget. However, how unique hit scales with compute remains poorly characterized, and methods focused on improving hit rate have produced mixed results. We propose a cluster-level view of binder design that decomposes unique hit scaling into two processes: discovering structural clusters through generation and discovering hits within those clusters through evaluation. This decomposition provides a predictive model of campaign yield and exposes a new axis for scaling: allocation of compute between generation and evaluation. We introduce a cluster-guided allocation (CGA) policy that estimates two processes online and allocates compute according to the expected unique hit yield per unit cost. Across 19 targets and three generators, the two-process decomposition characterizes the campaign well, and CGA outperforms naive sampling, showing consistent and predictable gains not seen in previous reward-tilting methods. Beyond these gains, the framework provides a mechanistic understanding of binder design scaling and reveals complementary opportunities.