Whilst ML-based regression models that predict IC50 values from pure substrate have been validated numerous times previously, we believe these methods are less suitable in the context of D2B. To develop any model that predicts the IC50 outcomes (values from regression, or active/inactive from classification) of pure substrate, pure substrate must first be obtained. However, two key features of D2B are 1) the utilization of crude, unchromatographed compound in biological assays and 2) the ability to perform ultra-high throughput screens on nanogram quantities of substrate. Both of these key features make the traditional ML IC50 prediction platform incompatible. It would necessitate several hundred compounds be made on larger scales to ensure enough material for isolation and subsequent IC50 evaluation; a slower and less high throughput method which runs contrary to the goals of D2B. Thus, we propose that our model, with its capacity for false negative detection as a consequence of training upon crude IC50 data, lends itself as a better fit into D2B campaigns.