The relationship between features and the performance of the GBT model could be used to detect cases where the prediction of the 19 F NMR chemical shift would be poor. Figure shows the prediction performance on CF motifs of a GBT model trained on the LEF4000 generating chemical shifts for tranche 2. A grid search determined that CF containing compounds with a Dice similarity of less than 0.8 or at least 3 unknown bits in its fingerprint should be sorted into a subset of samples with unknown features (see Table of the supporting information). The subset of compounds with only known features (Figure ) is visibly better predicted than the subset with unknown features (Figure ). Roughly a third of the data set made up the subset of known features. A little less than two-thirds of all samples were sorted into the unknown features subset.