In computational chemistry, solvent effects are modelled using implicit or explicit solvent models. The former represents solvents as a polarizable continuum, offering computational simplicity and efficiency. However, it fails to capture the contributions arising from solutesolvent interactions, including entropy and pre-organisation effects. On the other hand, explicit solvent models provide an atomistic representation of the solvent but at a much higher computational cost since they require the use of ab initio molecular dynamics (AIMD). This cost becomes significant when attempting to compute free energies, where extensive sampling is required to obtain statistically meaningful ensembles. Hybrid approaches, such as quantum mechanics/molecular mechanics (QM/MM), can alleviate this computational cost by describing only the reactive part at a QM level while the environment is described classically. However, the inclusion of mobile solvent molecules into the QM part creates additional technical difficulties, such as discontinuities at the boundary between the QM and MM regions. In recent years, machine learning-based potentials (MLPs) have emerged as powerful surrogates for existing modelling techniques, representing complex potential energy surfaces (PES) with an accuracy comparable to QM methods but at a significantly lower computational cost. 9,10 Since the pioneering work of Behler and Parinello (BP) on neural network-based potentials (NNP), 9 several MLP approaches have been developed, including models directly derived from BP, such as DeepMD 11 and ANI, 12 message-passing NN models, 13 such as when coupled with active learning (AL), where the MLP is retrained using new data collected based on preliminary versions of MLP, these methods can provide accurate potentials with much smaller data sets than traditional NN approaches. In this way, one can efficiently explore the chemical space required to describe reactions involving complex PESs.