DEL screening is not without its flaws. The process is highly susceptible to false negatives and, more concerningly, false positives. Thus, the resulting data has a much lower signal-to-noise ratio compared to other types of biological activity data typically used for modeling. Additionally, there is the issue of low chemical diversity within a DEL. Although a DEL may contain billions of unique compounds, it is generally assembled from only a few thousand building blocks, with limited chemistry used to link them . New methodologies need to address these limitations and improve the accuracy of future DEL-ML experiments. Given the limited data available, few attempts have been made to develop models specifically designed to learn more edectively from DEL data. The public release of new data through our open framework aims to drive innovation in this area and help overcome the multiple challenges DEL data presents for ML.