The workflow presented here builds on our previous work on automated AL strategy for modelling chemical reactions in explicit solvents (Fig. ). The AL cycle is initiated from a small training set of approximately 10 structures. These data consist of gas-phase molecules generated by random displacement from a QM-optimized structure or solvated clusters, obtained from MD simulations or random placement of molecules in a box. The structures are labelled with energies and forces computed at the reference level of theory. The initial training data set is extended using AL as follows: A first version of the MLP is generated and used to propagate several independent MD simulations, typically ten, for n 3 +2 fs, where n is the index of the MD run in the AL loop, starting from 0. From these trajectories, new structures are selected using a similarity selector, which identifies new structures to be included in the training based on the similarity between a global Smooth Overlap of Atomic Positions (SOAP) representation of data point p and configurations p ′ in the existing training set. The similarity vector, K, is defined as follows: