Our PriA-SSB case study revealed some challenges of virtual screening on large commercial chemical libraries and limitations of our current approach. Compound availability in large libraries can change, especially when manually obtaining quotes for the desired compounds. Real-time, automated access to available compounds and prices could improve the screening process. Despite this, high synthesis success rates are an advantage of our strategy of scoring synthesize-on-demand libraries. 68 of the 90 compounds we requested from Enamine were synthesized successfully. In contrast, most generative machine learning models 46-48 may propose appealing compounds that are difficult or impossible to synthesize. An additional limitation is that we were unable to directly compare the potencies of the active compounds in the training dataset and the hits found in the AMS and Enamine libraries.