After showing that an optimal ordering is possible but the active learning approaches still failed in the 1 cluster case, we investigated the 2 cluster case. We selected the third largest cluster as it contained the largest number of molecules while also maximizing the coverage of the chemical space of our model (Figure ), and created the "Clusters 02" dataset. The hypothesis is that the addition of the second largest cluster would help the model's ability to generalize and with an extra source of semi-related molecules could have a chance to improve the active learning selection process. We opted to only evaluate the performance of the NIG regression approach and only looked at a single split as we were continuing to use the greedy selection analysis and the larger dataset size constrained our computational resources. The results of this experiment are shown in Figure . The addition of the second cluster improved the model's final performance on the test set, and increased the maximal performance of the greedy selection, likely due to having more training data and the same size test set. However, active learning was still no better than randomly picking molecules, and the model saw no improvement on its ability to generalize. Due to both the computational demand, and it not performing well on anything except the dataset it is fit to, we do not perform the Greedy selection baseline on the remainder of our experiments.