Two parameter search algorithms are currently supported by DockOpt: grid search and beam search . Grid search explores the search space through a predefined grid of potential parameter value combinations. Due to its exhaustive nature, this approach can theoretically find the optimal parameter combination(s), given a sufficiently fine discretization of parameter space. However, when searching through anything but the coarsest resolutions of parameter space, this method can rapidly become computationally expensive to the point of intractability. In contrast, beam search narrows its search space by applying a selection criterion at each step to retain only the top fraction of candidate DOCK parameterizations for the next step. The range of considered values can be progressively refined, facilitating a more focused exploration of promising solutions. As a result, beam search may not explore all possible options, but it is more computationally efficient, as it constrains exploration to regions of parameter space likely to hold promising parameterizations, based on certain assumptions. For example, one assumption is that locally optimal choices (i.e., highscoring candidate solutions at each step) will yield globally optimal or near-optimal solutions. This may not always be true, as locally suboptimal choices can sometimes lead to better overall solutions.