We have showcased a dual machine learning framework that can expand the scope of D2B-compatiable reactions by deconvoluting reaction yield and potency and allowing for low yielding reactions to be included. The "Swiss Cheese" framework acted as a deconfounder of low yield from low M pro inhibition and resulted in the elucidation of three active compounds previously labeled as inactive. Additionally, we highlighted the framework's utility in a much broader screen of over 58,000 compounds; it would be challenging to validate such a number of compounds in a time-and cost-efficient manner. This in silico screen identified 7 new nanomolar potent compounds with valuable and non-deleterious structural motifs. We hope that future D2B and/or in silico screens can take advantage of this lightweight ML architecture which can be used in both low and high data environments to uncover new biologically valuable compounds and motifs.