Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries. As such, it has become an increasingly common approach in early-stage drug discovery. These screenings generate large amounts of data proportional to the size of the compound library used, which must be stored and filtered before visual analysis. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Although Ring- tail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box, its modular design allows for easy extension to support input file types from other dock- ing software, different storage solutions, and its incorporation into other applications. Ringtail’s SQLite database output can dramatically reduce the required storage by selecting individual poses to store and by taking advantage of the relational database format. We observed disk usage reductions of between 36-46 fold compared to raw AutoDock-GPU DLG output for a 50-pose docking and, and more than 3 times more efficient than achievable by only the reduction of stored poses. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ring- tail a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and both scriptable and modifiable to fit specific user needs.