A different approach was recently proposed by Zinovjev. There, instead of explicitly training on the QM/MM energy, the in vacuo energy of the QM part and the interaction between QM and MM (the embedding) are calculated by separate ML models. Below, we will refer to the model responsible for the embedding part of the total energy as "electrostatic ML embedding" or EMLE. It was shown that EMLE does not require QM/MM energies for training, but rather can be obtained by learning atomic properties of the QM system in vacuo and using analytic models of the electronic density and induction to describe the response of the system to the presence of MM point charges. This approach is based on the previous work by Bereau et al. where the interaction energy between two molecules was calculated based on their in vacuo properties, physics-based models and just a handful of tuneable parameters. The EMLE model could be seen as a limiting case of this approach, where one of the molecules is reduced to non-polarizable point charges. The proposed ML electrostatic embedding scheme has two important advantages. First, it employs a separate in vacuo potential for the QM part. Therefore, if a pretrained (or generic) MLP for the QM subsystem of interest exists, it can readily be employed in the scheme, without further training. Second, the scheme only relies on the information that is usually provided to a QM engine in QM/MM software: the positions of the QM atoms and the positions and partial charges of the MM atoms. This allows to package the prediction code (in vacuo QM + embedding) as "just another QM engine" and directly use it with existing QM/MM software without significant modifications. These two advantages are explored in the present work.