Notably, for two filters with relatively high prevalence in the library (Lipinski and Ghose), sampling i.i.d. from the trained policy attained a lower satisfiability rate and cluster count than the stratified random baseline. With diversity sampling, the number of satisfied clusters increased two to four-fold at higher diversity, substantially outperforming the baselines and HE. These results suggest that NGT's trained policy shifted the UnREAL distribution, enriching for desired properties, especially when those are rare, specific, and non-additive, as is common in drug design.