Finally, we tested database reduction methods employing supervised and unsupervised active learning. The data reduction slightly improves the NN model performance for the same data sizes compared to random selection. We speculate that this could be caused by the fact that the driving mechanism of cluster binding is hydrogen bonding, which gets better described with more data, but there is no particular data that would introduce hardto-model hydrogen bonds. Nevertheless, active learning appears to be a suitable tool for eliminating outliers. Furthermore, we find that the NN model trained only on energy, using ∆-learning, is able to utilize significantly more data than our previous KRR model. While we again confirm that KRR performs better for smaller sizes, NNs also can reach MAEs lower than chemical accuracy of 1 kcal/mol, while outperforming KRR with respect to the computational times required for both training and predictions. We envision utilizing NN models for a better understanding of cluster dynamics. This includes cluster rigidity, reorganization after formation, reorganization before fragmentation, or even reactions within the cluster or on its surface. Overall, we believe that NN modeling will play a pivotal role in future studies of atmospheric molecular clusters.