A recent DEL-ML competition hosted on Kaggle (BELKA) found that models trained on DEL data performed nearly randomly when predicting on out-of-distribution (OOD) data from the same DEL selection . The competition hosts concluded that none of the thousands of DEL models built were able to edectively generalize to chemicals distinct from the training set. Here, we observed similar trends, indicating that our DEL-ML models also struggled to accurately predict compounds dissimilar to the DEL PTEs within the training set (Table ). This is interesting considering that, at first glance, the chemical spaces occupied by OpenDEL and the Enamine REAL Space catalog do not appear to dider drastically (Figure ). However, DELs are built from a constrained set of building blocks, often numbering only several thousand unique building blocks. The large size of the DEL provides the illusion of diversity, but in reality, the chemical diversity of any given DEL is limited. This constraint may hinder the model's ability to generalize far beyond the DEL space, as supported by the drop in performance between the random and OOD validation results.