RegioSQM20 is developed and tested on 535 molecules for which the regioselectivity of bromination by EAS has been reported in the literature. The accuracy of the predictions with xTB and PM3 are roughly the same (ca. 91%), indicating that the same accuracy can be achieved with an open source approach. The inclusion of tautomers increases the success rate from 90.7% to 92.7%. RegioSQM20 is compared to two machine learning based models: one developed by Struble et al. specifically for regioselectivity predictions of EAS reactions (WLN) and a more generally applicable reactivity predictor developed by Schwaller et al. (IBM RXN). Re-gioSQM20 and WLN offers roughly the same success rates for the entire data sets (without considering tautomers), while WLN is many orders of magnitude faster. The accuracy of the more general IBM RXN approach is somewhat lower: 76.3%-85.0%, depending on the data set.