We find that we can identify active trisynthons reliably solely using the P(active) values of their constituent building blocks. We construct a simple decision tree which splits the data based on the P(active) value at one of the building block positions (Figure ) and evaluate the performance of the model using the metrics precision and recall (see Section 4.6). ). The area under the curve (AUC) of the precision-recall curve is 0.961, which is significantly higher than the AUC for a random guessing model, which is equal to the hit rate of the test set ( 10302 443380 ≈ 0.0232). Given the AUC of a perfect classifier is equal to 1.0, this demonstrates that using building block P(active) to predict the activity of trisynthons is highly reliable for this DEL data. A similar analysis can be performed for other DELs and targets to verify the fidelity of this analysis for alternative systems.