We have presented a robust and efficient AL strategy that utilises descriptor-based selectors to train MLPs for condensed-phase reactions. We evaluated the impact of three different selectors: energy, similarity, and distance on data efficiency and MLP accuracy using bulk water as a model system. Our findings reveal that descriptor-based selectors outperform the classical energy selector in both efficiency and accuracy. This is evident from the smaller number of configurations required with comparable MAD in energies and forces compared with energy selector.