We found that fine-tuning OMDB-trained models with the OCELOT database often increased the performance of the model. Fine-tuning improve the performance of models without angle featurization in the graph representation by 4.7 % in terms of MAE. However, it did increase the MAE of models with angle featurization by 18.2 %. We attributed this to the lack of data in the low bandgap region in the OCELOT dataset. Fine-tuning significanly improved the performance of both graph representations on the polymorphs of ROY, leading to a decrease in MAE by 77.0 % and 87.7 % for models with and without angle featurization, respectively. While neither fine-tuned model outperformed the baseline OMDB-trained model, this does show that fine-tuning can improve the performance compared to OCELOT-trained models. Fine-tuning had a mild effect on the general PAH101 dataset, with a MAE decrease of 1.5 % and 2.1 % for models with and without angle featurization, respectively. However, we did find that fine-tuning significanly improved the performance on the polymorph sets of the PAH101 dataset, resulting in a decrease of 11.7 % in MAE.