Multiple computational techniques may be used to evaluate ligand-protein binding, with docking being the most common and most amenable to high-throughput. The AutoDock Suite encompasses several freely-available programs which together account for over 40% of published virtual screening studies from 2015-2020, making it the most popular software choice for ligand-protein docking. The recently-released AutoDock-GPU (AD-GPU) program uses the AutoDock4 scoring function and features GPU-acceleration and an improved local search, making this program ideal for high-throughput virtual screening applications. Like many other big-data endeavors, virtual screening presents challenges arising from data storage and analysis. Even compressed docking results can occupy hundreds of gigabytes of storage. Additionally, some docking programs, including AD-GPU, address the stochastic nature of the docking search by performing multiple independent runs. This can result in degenerate (i.e. redundant) binding poses when the search function converges to nearly the same pose on multiple runs with slight score differences. Thus, a sensible method for storing non-degenerate data would improve the storage efficiency of virtual screening results.