Regardless of the cause, this presents a major challenge for future DEL-ML models: how do we accurately validate them? Current in silico validation approaches are less than ideal, as they rely on DEL PTEs and NTES within the training set for evaluation instead of the small molecules that will actually be screened by the model. This appears to result in overly optimistic assessments of model performance. On the other hand, while in vitro validation provides direct insight into how well the model performs on the screening set, it is expensive, time-consuming, and can only be done after the model is used.