Despite being faster than QM methods, ML models are still slower than classical force fields and often employ local descriptors with a cutoff radius, which neglects long-range interactions and challenges their scalability. This limitation has catalyzed extensive research into ML/MM frameworks, which can be divided into three main categories. The first employs a mechanical embedding strategy similar to early QM/MM methods, in which the coupling energy is computed assigning a set of fixed partial charges to the ML atoms (or any other way to approximate the electrostatic potential) and calculating the coulombic interaction between those charges and the MM subsystem. The second category address the influence of external potentials in the ML predictions by incorporating information of the MM subsystem as an extra input of the ML model. These methods have proven to be highly reliable in comparisons with QM/MM methods, although they require training on data specifically derived from QM/MM level simulations, which restricts their broader application. Finally, recent advances in hybrid ML/MM methodologies have introduced flexible frameworks like the electrostatic machine learning embedding (EMLE) engine, which integrates machine learning potentials with different MM backends for multiscale molecular dynamics simulations. These kinds of methods fall in the third category of ML/MM methods, and propose an intermediate approach, using the ML model to predict the in vacuo energy of a system while adjusting for environmental effects through a posteriori corrections to both ML/MM and pure ML energy (and forces) calculations. These methods seek to achieve the sophistication of electrostatic embedding, representing the most widely used QM/MM implementations, while maintaining transferability.