After DEL screening, the ML-ready fingerprinted DEL selection data were used to train two ensembles of light gradient boosted tree (GBT) models. The first ensemble, GBT-Small, was smaller and used only one type of chemical fingerprint (binary FCFP6) for the initial screening. The second ensemble, GBT-Large, utilized the same training data points but incorporated additional types of fingerprints for rescreening the initial virtual hits. Each model in the ensemble was trained on a diderent subset of the DEL data or a diderent type of fingerprint. All ensemble models voted collectively on the final predictions to reduce the risk of false positives (see Methods). To fully assess whether fingerprinted DEL selections provided sudicient information for successful model building, no information about the protein structure, DEL chemical structures, existing datasets, or known small molecule WDR91 ligands was used for model training.