In our previous work, we used a machine learning (ML) method to predict the 19 F NMR chemical shifts of compounds for focused 19 F NMR screenings we termed 19 Focused screens. That method was based on a Random Forest architecture but other architectures have been explored for 13 C and 1 H NMR or even for solid-state NMR . In the analysis of the predictions it became evident that the performance of the ML method was dependent on the similarity of the predicted molecules to the training data set. This was particularly pronounced in the extension of our in-house LEF4000 library by another 1530 molecules, which were chosen to explore new chemical space, and were therefore often dissimilar to the LEF4000 data being used as a training set. The strong outliers this generated would have been very difficult to deconvolute in a mixture of a 19 Focused screening.