Post virtual screen, potential hits were selected using a Tanimoto similarity cut-od of 0.4. These hits were then rescreened using the GBT-Big model. A final prediction score was assigned to each compound by taking the predicted probability of being active and subtracting the standard deviation among all ensemble mates. Compounds were then passed through a series of medicinal chemistry filters: removal of Michael acceptors, Lipinski's rule of 5 , Ghose method 50 , Veber method 51 , Egan method 52 , and Muegge method 53 . Compounds that contained Michael acceptors were automatically removed. Compounds were assigned a "MedChemScore" based on the number of filters that were passed. Those that failed to pass 4 or more filters were removed. Finally, the nominations were clustered using FCFP4 fingerprints and the "LeaderPicker" from the SimDivPickers module of RDKit 2023 . Clusters were ranked based on size and overall score. Nominations we're pulled from each cluster using the following rules: