In this work, we propose a general AL strategy for training MLPs to model chemical processes in explicit solvents and, more generally, large systems with complex PES. To achieve this, we employ an AL loop combined with descriptor-based selectors. In contrast to the metrics described above, these selectors use molecular descriptors, such as Smooth Overlap of Atomic Positions (SOAP), to assess whether the training set accurately represents the chemical space of interest. By examining the SOAP descriptor space for the structures in the training set, we demonstrate that these selectors provide a general metric applicable across different MLP approaches at a low computational cost. The effectiveness and versatility of the selectors are showcased using the linear ACE model. Firstly, we investigate a simple water box as a test case, evaluating and comparing the performance of various selectors in terms of the quality of the potential and data efficiency. Building upon these findings, we further apply our strategy to study the Diels-Alder (DA) reaction between cyclopentadiene (CP) and methyl vinyl ketone (MVK) in both water and methanol. Through this example, we illustrate the transferability of our approach to accurately model chemical reactions in different solvent environments. The generality and computational efficiency of our approach make it well-suited for a wide range of applications, contributing to the advancement of MLP in various fields. By harnessing its capabilities, accurate and efficient modelling of chemical processes in explicit solvents becomes attainable. This enables a deeper understanding of solvent effects and facilitates the investigation of more complex processes in solution.