Many useful reactions are underutilised in synthetic organic chemistry because of an inability to predict the regioselectivity of the reaction and there is thus an increasing interest in developing regioselectivity prediction methods for such reactions. Recent examples include nucleophilic and electrophilic aromatic substitution reactions, Diels-Alder reactions, Heck reactions, 12 radical C-H functionalisation of heterocycles, and reactions such as as alkylations, Michael additions, and aldol condensations that proceed through proton abstraction. These methods have been based on either quantum chemical (QM) calculations, machine learning (ML) trained on experimental data, 8,10-12 or a combination of the two where QM has either provided descriptors for the ML model or used to augment the training data. However, these approaches have rarely been compared on the same dataset. In this paper we present an ML model (RegioML) that predicts the regioselectivity of electrophilic aromatic substitution (EAS) reactions using QM charges. We compare the performance of RegioML to RegioSQM20 6 -a QM-based predictor for EAS regioselectivity -for the same dataset and discuss how QM-based predictors can be used to augment sparse experimental datasets. We focus in particular on the precision and recall of these methods for in-and out-of-sample datasets.