The strategies involving the inclusion of the low-fidelity labels or embeddings can be trivially extended to a setting with more than two fidelities. Concretely, a separate model is trained for each lower fidelity (here, ZINDO and PBE0), and the corresponding labels or embeddings are added to the high-fidelity model (here, GW) as usual. We have evaluated the direct inclusion of the low-fidelity labels, as well as embeddings generated by sum and adaptive readouts, both individually (i.e. only ZINDO or PBE0 at a time) and jointly (i.e. both labels or embeddings are concatenated to the internal molecular representation) and we report the results in Figure . Interestingly, the direct inclusion of the labels provides only small (less than 10% MAE decrease) or no performance uplifts, an observation shared with the outputs of the sum readout-based low-fidelity models. On the other hand, models leveraging neural embeddings provide significant improvements for ZINDO and PBE0 individually, despite the relatively low correlation between ZINDO and GW in particular (Figure ). Furthermore, models jointly using ZINDO and PBE0 embeddings perform slightly better than PBE0. This is an interesting result as ZINDO is a relatively crude approach that is by definition more approximate than PBE0.