We first began by identifying false negatives, these so-called "hidden gems". These are compounds which, by nature of their low yield, have been labeled with low inhibitory activity. In a traditional D2B workflow, these compounds would be either a) excluded prior to the assay screening due to their low yields or b) require some level of scavenger-assisted separation to normalize the concentration of the potentially active compounds. A Gaussian Process and Random Forest regressor were individually trained on the experimental M pro inhibition data from the 300 crude amides (Figure , Table ). Due to the conserved nature of the acid, the Morgan fingerprint of the amine moiety was used as the featurization of each compound. The predictions for both models, visually, lead to an easy distinction for true positives and true negatives: molecules that have high yields and high or low activity, respectively. However, we can also identify potential false negatives ("hidden gems"): compounds that were determined by to be structurally represented in the high activity regime, but experimentally determined to have low yield and low inhibitory activity (Figure , Figure , Figure ). The top 5 compounds that were determined to meet the aforementioned criteria (high predicted activity by both Gaussian Process and Random Forest, low experimental activity, low yield as determined by LC/MS) were selected as promising false negatives. These compounds, 3 -7, had report less than 35% M pro inhibitory activity via the fluorogenic assay and a yield under 50%. Of the five compounds, 4 were able to be synthesized in suitable purity. Whilst compound 6 was indeed inactivate (deconvolution failed), compounds 4 and 5 showed high nanomolar to low micromolar inhibitory activity and 7 and boasted an IC50 value of 81 nM, ranking it within the top 20 most potent D2B hits (Figure , Figure ). This result showcased the ability of the "Swiss Cheese" framework to find new promising molecules that would be typically bypassed. Considering the ease of implementation and the low data requirement to run the system, we believe it is a low risk, high reward strategy to augment established D2B pipelines.