We used machine learning to identify the most promising drug candidates based on their strength of binding inside the tunnel and their likely ability to prevent the s-glycoprotein trimer from undergoing functionally necessary conformational change. Although we only selected 10 inhibitors here for the sake of brevity, this number could easily be increased. CaverDock is fast enough to analyse an even higher number of snapshots to cover the protein's conformational space more comprehensively or to examine a significantly greater number of ligands. Importantly, this workflow is currently being made available on the CaverWeb tool to enable automated virtual screenings of the ZINC globally approved drugs dataset.