While the WLN based method is considerably faster, the RegioSQM approach may perform better for ring systems that are not well represented in the training set. Unfortunately, the training sets used to develop the WLN based method is not publicly available, so this hypothesis is difficult to check. Another difference is that RegioSQM finds two or more atoms with roughly equal reactivity in about one fourth of the molecules while this almost never happens with the WLN based method. This could reflect the possibility that the literature and especially patents tend to report only the desired product even if other products are observed. This "bias" is then introduced to machine learning models since they are developed based on data that are extracted from these sources. For example, 21 out of the 32 molecules that fail with WLN (without considering tautomers) has more than one experimentally observed reactive site, compared to 6 out of 21 for RegioSQM20*. In fact, out of the 38 molecules in our data set with two or more experimentally observed reaction sites, WLN makes correct prediction for only two molecules, while RegioSQM20* makes correct predictions for 25. RegioSQM20 thus could offer a useful complement to machine learning based methods in some cases.