Within the AutoDock suite, Raccoon and Raccoon2 previously provided graphical user interfaces for virtual screening with AutoDock4 and AutoDock Vina, respectively 4,8 with some degree of data persistence. However, neither of these tools scale with sufficient efficiency to handle very virtual screenings (>1M ligands), nor do they support the recent AD-GPU program. A recently reported tool LigGrep, 9 provides one of the few open-source option for filtering virtual screening results, including those created with AutoDock Vina. This tool is designed around distance-based filters and takes advantage of multiprocessing to minimize processing times, but it does not offer any data persistence, requiring users to parse raw docking result files every time. Therefore there is a still unmet need for tools that are able to address the challenges presented by large result collections, while facilitating the process of visual inspection with robust, reproducible, and user-friendly features. This is the motivation for the development of Ringtail, a modular Python package designed to both store output files from a virtual screening as a portable SQLite database and extract selected results from said database. With Ringtail, we seek to address the data storage challenges associated with virtual screening results and enhance the user's ability to easily explore large virtual screening datasets with a single and extensible tool. This design presents a number of advantages. First, the use of the file-based SQLite database to store results allows us to access most of the features of a relational database without requiring the installation of a dedicated server. SQLite is open-source, and is considered the most widely deployed and used database engine, 10 and is natively supported by Python since version 2.5. Storing results using a persistent storage solution delivers significant run-time advantages over text-based filtering for both the CPU-based parallel file parsing of the raw input and the subsequent filtering steps. Moreover, this allows users to automatically capture filtering operation for reproducibility.