We also investigated different strategies for adding candidates to the training set, beginning with comparing active learning random sampling and highest uncertainty sampling. The former involves evaluating each candidate once, in random order, and adding those above the uncertainty threshold to the training set, retraining when reaching the batch size. By contrast, highest uncertainty sampling, which is the most common strategy for MLPs, evaluates all remaining candidates each iteration and adds the candidates with the highest uncertainty to the training set. Additionally, we implemented a strategy combining highest uncertainty and farthest point sampling. This strategy, which sampled candidates above the uncertainty threshold by FPS starting from the candidate with the highest uncertainty, was intended to reduce redundancy in the training set that may arise when sampling by highest uncertainty with a batch size greater than one.