To avoid many of the issues related to the synthesis of undesirable compounds, "virtual screening" of curated small molecule catalogs using machine learning (ML) models has become increasingly popular . Computational hits predicted to be active are purchased from commercial vendors and tested in binding assays for activity. Recently, McCloskey and colleagues pioneered the use of data from DEL screening, in which the encoded molecules were classified into binders and non-binders, to train ML models of bioactivity. These models were then used for virtual screening (VS) of large commercial libraries of organic small molecules, enabling the selection, purchase, and testing of putative hit compounds. Critically, in contrast to the conventional od-DNA follow-up protocol for DEL hit validation, the DEL-ML pipeline not only avoids the need to resynthesize the original library compounds without DNA tags, but it also permits filtering of virtual hit compounds based on favorable chemical properties before selection and purchase. Since the pioneering DEL-ML study by McCloskey and colleagues , several groups have shown that this approach can be applied to a wide array of target classes to identify chemically-tractable small molecule hits .