In its pre-release version, DockOpt used grid search as its search algorithm, exhaustively testing all possible combinations of parameters values, with each parameter taking a pool of possible values. For example, distance-to-surface values for electrostatic thin spheres 52 might be the set {1.0, 1.1, …, 1.9} and distance-to-surface values for ligand desolvation thin spheres might be the set {0.1, 0.2, …, 1.0}, resulting in a Cartesian product space of 10 × 10 = 100 combinations. This strategy works well enough for small numbers of parameters with a limited number of values per parameter. However, it is too inefficient to serve as a general search algorithm, as it would subject users to exponentially increasing computational cost when exploring higher dimensional parameter spaces at finer resolutions. Consequently, the latest release of Pydock3 configures DockOpt to use beam search by default. Below, we demonstrate that our implementation of beam search consistently finds superior parameterizations to those found by grid search using comparable computational expense.