We report the first application of this open framework to the discovery of protein ligands, specifically for the WD40 repeat-containing protein 91 (WDR91). This human protein plays a critical role in regulating early-to-late endosomal maturation and tradicking, processes essential for cellular functions such as nutrient uptake, signal transduction, and membrane protein recycling . Previously, a DEL-ML method was used to discover first-in-class binders of WDR91, but the DEL data and ML models remained confidential . Thus, the key objective of this study was to determine whether the limited chemical representation used in our open framework could still support the development of successful open-source ML models capable of identifying potent binders in commercial chemical libraries. We screened HitGen's OpenDEL TM (~3x10 9 unique compounds) against WDR91, and all screening data, including chemical fingerprints of DEL compounds and enrichment data, were deposited in the AIRCHECK database. Using these data, we trained a simple, gradient boosted tree (GBT) ML model, which was subsequently employed for the virtual screening of the Enamine REAL Space 27 in a fully automated pipeline without human intervention to predict potential binders. Single digit micromolar binders were confirmed by surface plasmon resonance (SPR) assays, and co-crystal structures of experimental hit compounds bound to WDR91 were determined.