We have developed an enhanced sampling approach that allows us to explore combinatorially large spaces of inhibitor designs in a way that automatically biases the simulation toward inhibitors with higher affinity for one or more targets. This approach---based on expanded ensemble simulations and made possible by a new nonequilibrium Monte Carlo algorithm we developed---promises to provide a time- and cost-effective solution to the problem of optimizing small molecules for affinity. By restricting the space of compounds to those accessible from a given set of commercially-available starting materials and a library of common synthetic transformations, we aim to propose a set of compounds that have a high likelihood of increased potency and are likely to be readily synthesizable.