The trained model was then used to screen the entire 2023 Enamine REAL Space catalog (~37 billion compounds) 27 (Figure ). Chemical structures for all compounds in this catalog were fingerprinted using the same methods used for the DEL datasets. An initial screening with a smaller model (see Methods) identified approximately 19,000 compounds as potentially active, spanning 280 chemical clusters and covering a broad chemical space (Figure ). Common medicinal chemistry filters, such as Lipinski's Rule of 5 , were applied to eliminate undesirable chemical structures. Additional models were then used to further assess prediction accuracy. From the top-performing clusters, 50 compounds were selected for purchase, originating from 8 distinct clusters (Table ). Notably, the entire screening and nomination process was conducted autonomously, with no visual inspection of cherry-picking by a chemist or human intervention at any stage.