Second, the fraction of active or promising molecules in the CSL may not grow at the same rate as the full library. For example, in-stock screening libraries can contain 19,000-fold more bio-like molecules than virtual libraries with important implications for the success of virtual screening. The likelihood of finding high-scoring but inactive compounds with predictive AI increases as large swaths of chemical space are now out-of-distribution for the models. Novel functional groups may have unknown bearings on ADME and molecular properties which may come to light only in later stages of the drug discovery pipeline. Finding developable hits, e.g., potent and soluble compounds is more challenging as libraries continue to grow much faster than the set of known active compounds.