Machine learning-based potentials (MLPs) offer an efficient alternative to electronic structure methods used in AIMD simulations. MLPs map a set of molecular structures to energies and, often, forces, leveraging various machine learning (ML) architectures such as neural networks (NNs), graph NNs, kernel-based approaches, and linear regression techniques. MLPs have found applications in a wide range of areas, including the study of organic molecules, the exploration of materials and the reproduction of the physical properties of bulk water. However, their capability in reaction modelling remains underexplored, with only a few studies investigating the dynamics of chemical reactions, typically in the gas phase and implicitly modelled solvent, such as pericyclic and photochemical reactions. The reported examples of organic reactions with an explicit description of the solvent include works by the groups of Parrinello, Meuwly, Corminboeuf, and our recent work on modelling a S N 2 reaction in water and a Diels-Alder reaction in water and methanol. The use of MLPs in reaction modelling is limited by the high computational costs of generating diverse training data sets representative of the PES, including high-energy TS regions. Common data generation strategies include dynamics sampling through AIMD from QM-optimized TSs or with enhanced sampling methods, or employing Nudged Elastic Band (NEB) and normal mode sampling techniques are used to collect representative configurations on the PES. Active learning (AL) can further enhance the training dataset by iteratively exploring the PES using molecular dynamics (MD) driven by preliminary versions of MLP (MLP-MD) to identify under-represented configurations, improving MLP performance while reducing structural redundancy.