We train a decision tree classifier using the P(active) and cluster information of every building block in the training set as input (training precision: 0.960, training recall: 0.926) and then apply the classifier to our holdout set. We find that every method outperforms random guessing by at least an order of magnitude (Figure ). In addition, using the cluster nearest neighbor to approximate untested building block P(active) gives the best result for predicting the activity of trisynthons (AUC: 0.799; averaged over 50 random trials) (Figure ). This finding further supports the argument that on average, chemically similar compounds have greater probability of similar activity. Using similarity scoring and a decision tree model, we predict the activity of trisynthons containing building blocks not seen in the training set at a rate more than an order of magnitude greater than random. The performance of this approach demonstrates that even relatively simple models can estimate the activity of new compounds when applied to DEL selection data. As the intersection between machine learning and DELs grows, we challenge researchers to pay attention to straightforward models like the one employed here and evaluate whether more complex machine learning methods perform significantly better.