The tmQM wB97MV dataset is presented, which takes structures from tmQM, recomputes their electronic energies at the ωB97M-V/def2-SVPD level of theory, and filters out some of the erroneous structures that are missing hydrogens. Several GNNs benchmarked on this dataset show that ML models are able to predict transition metal complex energies fairly well, and that their performance improves when trained on tmQM wB97MV compared to tmQM. In particular, tmQM wB97MV alleviates the chemically distinct underprediction observed with tmQM. Using a fine-tuning approach from large datasets in heterogeneous catalysis further improves performance, which is promising for the concept of large, generalizable models. Despite improved results with tmQM wB97MV, the dataset as-is will not be sufficient for training ML models capable of screening transition metal complex catalysts because it does not span all species relevant to reactions, where the most glaring omissions are open-shell complexes and reactive intermediates. Additionally, the inability of the ML models trained to account for different charge and spin states will be a significant hindrance in predicting homogeneous catalytic reaction pathways where redox often plays a central role.