To analyze the synergy between QM and GBT we first generated a learning curve for both using the CF motifs in Enamine data. Figure shows learning curves for GBT and the linear regression correction that converts QM shielding constants into chemical shifts. Both were trained on increasing amounts of randomly sampled FDS-1000 data and used to predict FFL-d6 compounds. The linear regression correction of the QM method showed very little dependence on the training set. After 100 randomly sampled training data points the RMSE of the linear regression correction changed by less than 1%, with the largest changes ending after 50 points. GBT continually improved with training set size and eventually outperformed QM after around 500 data points. This suggested that in situations with less data, our QM workflow is more robust, but it is eventually outperformed by ML. QM differs from the ML methods in several fundamental ways. One is that QM by its use of molecular geometries can discriminate between stereoisomers. Figure shows all four possible stereoisomers for a molecule with two stereocenters. According to the QM calculation, the CF3 motif can have 19 Another aspect that QM handles is conformer effects. Figure shows a population plot of conformers as well as the corresponding conformers for a CF motif. There was a significant difference in chemical shift between the most likely and the least likely conformers in the ensemble. None of the chemical shifts of the conformers coincided with the measured chemical shift. The most likely (i.e., the lowest energy) conformer was associated with a chemical shift of -131.44 ppm compared to the measured chemical shift of -134.66 ppm, an error greater than 3 ppm. A Boltzmann average over the conformer ensemble produced a better chemical shift prediction of -135.60 ppm, which is an error of around 1 ppm. This highlights the importance of sampling more than just the lowest energy conformation for precise 19 F NMR chemical shift predictions. of the supporting information. This changes multiple bits in the fingerprint and makes it harder for the GBT model to accurately predict these examples. The FFL-d6 quinolines have a 19 F NMR chemical shift in the range of -107.73 ppm to -109.93 ppm and are all predicted at around -119.47 ppm to -120.87 ppm, a consistent 10 ppm error. The QM workflow can more accurately assess the nature of the ring systems involved and consider the effect that changes in substitution make, which leads to better predictions of -108.37 ppm to -111.76 ppm for the FFL-d6 quinolines. The QM workflow is not as dependent on approximations as the ML method and can therefore generalize better.