This bulk of DEL data oders unique opportunities for applying advanced ML methods, but until now, it was only accessible to DEL companies. In this pilot study, we reported the first application of an open science DEL-ML framework to discover novel binders of human protein WDR91. Although explicit chemical structures of DEL molecules were masked, they were represented using multiple chemical fingerprints, commonly employed in cheminformatics for chemical similarity searches and ML model development. We demonstrated that this representation was sudicient for developing statistically significant and validated ML models using the popular light GBT algorithm. These models were then used to virtually screen 37 billion compounds from the Enamine REAL Space library. We tested 48 ML-nominated hits and identified 7 with micromolar binding adinities (15% hit rate). Cocrystal structures of WDR91 bound to selected compounds identified the compounds' binding mode and highlighted the relevance of the pharmacophores identified by the ML models. This experimental success suggests that the public release of DEL data can stimulate the development of ML models to discover novel chemical starting points for subsequent optimization into drug candidates.