  We present a new method for understanding the performance of a model in
virtual drug screening tasks. While most virtual screening problems present as
a mix between ranking and classification, the models are typically trained as
regression models presenting a problem requiring either a choice of a cutoff or
ranking measure. Our method, regression enrichment surfaces (RES), is based on
the goal of virtual screening: to detect as many of the top-performing
treatments as possible. We outline history of virtual screening performance
measures and the idea behind RES. We offer a python package and details on how
to implement and interpret the results.
