Due to the importance of Molecular Dynamics simulations within fields such as thermodynamics, numerous methods have been developed to speedup the force calculations, which typically dominate the runtime. None of these methods are, however, optimal for every molecular model, on every hardware, and for every distribution of molecules. For non-HPC-expert simulation developers, choosing and then implementing the best method for their particular simulation is a challenging task. Furthermore, different regions of the simulation can have different molecule distributions with different optimal methods, and these can change as the distribution changes. A solution to this problem, AutoPas, is an open-source, C++17, particle simulation library, which can be used to build a particle simulator that automatically selects and tunes the optimal algorithmic configuration from its internal library. It can optimise for either time or energy. This poster focuses on efforts to improve its performance with complex multi-site molecular models, where the amount of data required per molecule may vary, as well as algorithm selection methods that use data-driven and expert-knowledge-based approaches to reduce the overhead of its selection process. This poster highlights the practicalities of such approaches being used by non-HPC expert users.