In this article we will train single and multitask models on four publicly available datasets (ANI-1x, 20 ANI-ccx, OrbNet Denali 25 and SPICE ) and then evaluate the performance of all of the output heads on single point energy tasks covering a range of interaction types. We will train to energies and dipole moments. Some of the datasets also have labeled forces but we do not use this data for consistency. Some of the datasets also lack dipole information but we view this data as secondary in importance. First we will reproduce the results of Smith et al. and show that we can perform multitask training as an alternative to pre-training a model on ANI-1x and then freezing layers and re-training to the ANI-ccx dataset. We will then show that adding the OrbNet Denali dataset can improve the performance of the ANI-1x and ANI-ccx predictions on torsion profiles of both neutral and ionic molecules even though the ANI-1x and ANI-ccx datasets contain no ionic examples. Finally we will show that adding the SPICE dataset improves the performance of intermolecular interactions of the ANI-1x, ANI-ccx and OrbNet Denali outputs, as measured by errors on the popular S66x8 dataset. 27,