Virtual screening of ultra-large synthesize-on-demand libraries presents both opportunities and new challenges. One concern is the possibility of getting buried by false positives due to biases or errors in the scoring model that go undetected during model validation. Models may erroneously recognize uncommon or exotic chemical features as important to activity due to limitations or mislabeled instances in a ligand-based model's training set or unsuitable parameters in a scoring function. Compounds with problematic features could be rare enough in smaller libraries that they account for only a small number of false positives and go unnoticed during model development. However, the sheer size and chemical diversity of synthesize-on-demand libraries could cause the top scoring compounds to be dominated by such errors.