The ECFP4 similarity in Figure is given as a reference to a fingerprint describing the whole molecule. It supports that the proportion of dissimilar molecules in tranche 2 is higher than in the FFL-d6. It is interesting to note that all molecules in both sets are quite dissimilar using the ECFP4. The F-FP-7, which is rooted at the fluorine, not only on average finds training molecules with a greater similarity for test molecules but also produces in a more even distribution of similarity. Despite the lower overall performance of QM and the lower number of samples processed by QM in the split of the Enamine set, the synergistic QM assisted GBT method still performed better than GBT or QM by themselves in RMSE. The difference in MSE between QM and QM assisted GBT was statistically significant (p = 0.019). The MAE did not change much in comparison to GBT but was improved in comparison to QM. The Kendall's Tau dropped slightly in comparison to GBT, which highlights the importance of splitting data sets as precisely as possible. Processing everything by QM would have resulted in lower overall performance in this case. Note that the splitting that was performed on the FFL-d6 used the same Dice similarity and number of unknown bits thresholds as the one performed on tranche 2 of the LEF. The parametrization of the sorting into known and unknown subsets on the timesplit in the LEF had been stable enough in this case to extrapolate to a different data set. Results for CF2 and CF3 can be found in Section 6 of the Supporting Information.