When looking at the ability of transferring optimal hyper-parameters across related training data, we observe similar effects found in the USPTO-50k models but more pronounced. The randomly initialized baseline model has a 6% higher top-1 and 4% higher top-10 accuracy over the smaller randomly initialized model with hyper-parameters optimized on accuracy from the UPSTO-50k dataset (Table ). The decrease in sample time and carbon footprint was also less pronounced between the accuracy model and the baseline model. The decrease in accuracy was larger in the randomly initialized model with parameters optimized for RCS, where accuracy dropped to 29.8% and 57.8%, for top-1 and top-10 respectively. This constitutes a 16.7% and 14.9% drop in accuracy from the baseline model for top-1 and top-10 accuracy, respectively.