In this study, we proposed and explored the concept of open science DEL-ML framework as a nearfuture scientific approach for generating and utilizing ultra-large chemical bioactivity datasets, on the scale of billions of compounds, created through DEL technology and deposited in the publicly accessible AIRCHECK database. On a per-target basis, this public dataset is already far larger than any currently available in the public domain. As a reference point, the specific DEL library screened for WDR91 target protein in this report comprised 3 billion molecules, with a subset of active and inactive compounds forming a dataset of approximately 375,000 compounds used for ML model training, all tested in the same bioactivity assay. In contrast, the largest publicly available knowledge base, ChEMBL 40 , spans 2.4 million compounds and 20 million bioactivity measurements across 15,000 targets. While PubChem 41 formally oders more bioactivity data in toto, the target-or assayspecific data is relatively small and prone to mis-annotations that can hinder ML model development. Notably, both ChEMBL and PubChem include data obtained from diderent laboratories using various screening or binding methods, limiting their use for target-or assayspecific ML model development. In contrast, target-specific DEL data is typically generated in a single laboratory where reproducibility can be controlled, making it more ideal for ML-driven discovery.