We think this project addresses the biggest limitation in democratizing DEL-ML technology: the lack of large, high quality DEL datasets. We demonstrated that even when only chemical fingerprints were shared, as in this case, sudicient information was present to enable the discovery of novel chemical binders through the DEL-ML-VS approach. By making such datasets publicly available, we can foster the development of better DEL-ML models and facilitate the in-depth exploration of big data analysis methods within the computational science and ML communities. These future studies will be able to address some concerns discussed in our proof-of-concept investigation regarding the low generalizability of common DEL-ML modeling approaches as well as low concordance between in silico vs in vitro model validation results. We expect that public release of large DEL datasets will allow the wider scientific community to address these, and other challenges related to building successful DEL-ML models. We hope that the open DEL-ML framework and its successful application to WDR91 ligand discovery described in this study will stimulate greater engagement of the ML community in cheminformatics research and support further democratization and much needed acceleration of drug discovery.