It is thus encouraging to observe that we are able to obtain similar uplifts in predictive performance for the augmented models on the previously unanalysed 37 multi-fidelity datasets (Figure ). More specifically, the augmentation with SD labels leads to uplifts, on average, from 0.219 to 0.285 in R 2 for RF (30.27% uplift), from 0.228 to 0.279 for SVM (22.45% uplift), and from 0.244 to 0.312 for deep learning (27.46% uplift). Similarly, for the SD embeddings augmentation, we observe uplifts in R 2 , on average, from 0.219 to 0.245 for RF (12.05% uplift), from 0.228 to 0.252 for SVM (10.34% uplift), and from 0.244 to 0.277 deep learning (13.11% uplift). This lower performance compared to the SD labels augmentation should be balanced against its unique ability among these methods to produce predictions for unobserved molecules. (f )