While recent successes in DEL-ML hit discovery are promising, all large-scale published DEL-ML campaigns to date have relied on proprietary DEL data and confidential ML pipelines. Currently, only a handful of DEL selection datasets with explicitly revealed chemical structures are publicly available. Almost all of these datasets were derived from relatively small, targeted libraries rather than the larger, more diverse DELs typically used in proprietary discovery campaigns . The largest publicly available dataset came from the BELKA Kaggle competition this year, but it was limited to just three protein targets . To further advance the field of DEL-ML for virtual screening and democratize this technology, there is a critical need for public access to large DEL screening datasets, in a form that provides sudicient information to build ML models. These datasets can often be in the form of chemical fingerprints suitable for training ML models without requiring the disclosure of the proprietary structures of the DNA-encoded compounds.