As seen in Figure , the false positive rates of these models are low on the sites where they are most confident; the effect of including the electronic structure features in model training in this scenario is that it expands the confidence range in which the models can make accurate predictions. This behavior highlights the nuance of when additional accuracy is necessary for HTVS: if the goal is to find only a few active catalysts among many, a lower accuracy model that incurs lower computational cost may be completely sufficient as long as the false positive rate is not too high. If the goal is instead to find a more complete fraction of all active catalysts or to have a high likelihood of finding the very best catalysts, choosing a more accurate yet more expensive model may be worthwhile.