  High-throughput virtual screening is an indispensable technique utilized in
the discovery of small molecules. In cases where the library of molecules is
exceedingly large, the cost of an exhaustive virtual screen may be prohibitive.
Model-guided optimization has been employed to lower these costs through
dramatic increases in sample efficiency compared to random selection. However,
these techniques introduce new costs to the workflow through the surrogate
model training and inference steps. In this study, we propose an extension to
the framework of model-guided optimization that mitigates inferences costs
using a technique we refer to as design space pruning (DSP), which irreversibly
removes poor-performing candidates from consideration. We study the application
of DSP to a variety of optimization tasks and observe significant reductions in
overhead costs while exhibiting similar performance to the baseline
optimization. DSP represents an attractive extension of model-guided
optimization that can limit overhead costs in optimization settings where these
costs are non-negligible relative to objective costs, such as docking.
