The architecture of the EMLE method and the proposed implementation allows to combine different embedding and in vacuo models to estimate the importance of the energy components in the final combined potential. For the in vacuo ML potential, two models were used: 1) ANI-2x, which is a generic ML potential; and 2) an in-house trained potential specific for ADP conformational changes in vacuo (Δ-ML, see below). For embedding, three options were used: 1) the "MM" embedding model with the interaction between ML and MM being treated fully at MM level (using the fixed MM point charges from ff19SB for ADP, the ML region); 2) the "EMLE static" model where the induced term of Eq. 4 is neglected, and 3) the full generic electrostatic machine learning embedding model (or "EMLE total" model), which includes electrostatic and induction terms. All the six combinations of the embedding and in vacuo models were tested to evaluate the impact of both the in vacuo and the interaction potentials on the quality of the free energy landscape of ADP in water.