To test the applicability of the multi-eGO approach, we applied it to four systems of increasing complexity. The first test case is the binding of benzene (BNZ) to the L99A mutant of the bacteriophage T4 lysozyme (LYZ) , which is widely used to benchmark computational methods of protein-ligand binding . LYZ is a small enzyme (162 amino acids in the case of T4) that catalyzes the hydrolysis of 1,4β-linkages of cell-wall peptidoglycans and consists of two domains (N-terminal and C-terminal). The L99A mutation produces a small apolar cavity in the C-terminal domain, which allows the binding of small nonpolar ligands such as BNZ with an unbinding free energy ΔG=21.7±0.8 kJ/mol . The second and third systems regard the binding of dasatinib (DAS) and PP1 to the c-Src kinase (SRC). Dasatinib is a chronic myeloid leukemia drug that has nM affinity for several tyrosine kinases, including Src, whereas PP1 is a Src kinase inhibitor with a lower IC50 value . These two molecules allow us to explore the effect of complex (in terms of internal degrees of freedom) ligands and strong binding free energies . The fourth system follows the binding of the small molecule 10074-G5 to monomeric Aβ42, which is an intrinsically disordered protein (IDP) whose aggregation is associated with Alzheimer's diseases, allowing a binding process, dominated by conformational dynamics, to be tested . Our results show that multi-eGO can quantitatively estimate the binding free energies of benzene, dasatinib and PP1, reproduce their hypothesized binding mechanisms and, importantly, allow a quantitative and systematic comparison of different simulation techniques. For Aβ42:10074-G5, we demonstrate that multi-eGO can describe extremely flexible systems, and single-molecule simulations and concentration-dependent simulations may be compared, i.e. models that closely mimic an in vitro experimental setup. Based on these results, we propose multi-eGO as an in-silico platform for ligand binding studies for applications in method development, benchmarking, and integration into drug screening pipelines. DES-Amber force field, we rescaled the AM1-BCC-derived charges for the ligands by a factor of 0.9. All systems were prepared at pH 7.4 and in the presence of Na + and Cl -ions to maintain physiological salinity (150mM) and to neutralize the total charge of the system. All systems were subjected to energy minimization using the steepest descent algorithm until the maximum force converges to a value < 1000 kJ/(mol nm), followed by a conjugate-gradient minimization until the maximum force converges to a value < 100 kJ/(mol nm) and a LBFGS algorithm until the maximum force converges to a value < 10 kJ/(mol nm). Subsequently, the minimized configuration was relaxed for 2 ns at a constant pressure of 1 bar and constant temperature, keeping the protein atoms restrained to their minimum energy configurations. The simulations used the leapfrog algorithm with a time step of 2 fs and LINCS restraints for hydrogen atoms. The cut off for non-bonded interactions was 1 nm, using PME for long-range electrostatics. Temperature and pressure were controlled by stochastic velocity rescaling and cell rescaling 52 algorithms, respectively. The number of replicates and simulation times are reported in Table . All simulations were run using GROMACS 2022 . LYZ was modelled starting from the PDB code 1L84 structure and simulated at 300 K. Training the LYZ:BNZ complex was performed by inserting one BNZ in the binding pocket, and 4 BNZ outside, in order to explore its interaction with the protein surface. SRC was modelled using the PDB code 1Y57 structure and simulated at 310 K. To train the holo complex of SRC:DAS/PP1, 1 molecule (either DAS or PP1) was inserted in the binding pose and 4 ligands were added to the solvent to explore the interaction with the protein surface. To train our multi-eGO model, the holo training trajectories were removed from the non-equilibrium portions, consisting of irreversible aggregation of three or more ligands with each other, as previously observed . MD simulations of apo and holo Aβ42:10074-G5 were carried out as previously described . Briefly, the apo simulation was carried out at 278 K using the CHARMM22* forcefield and the TIP3P water model . The holo simulation was performed under the same conditions, parameterizing the ligand 10074-G5 using the Force Field Toolkit 59 . Multi-eGO Simulations. Multi-eGO is a multi-ensemble hybrid transferable/non-transferable force field . The functional form is shared with conventional molecular mechanics force-fields except for using only LJ for non-bonded interactions. Bonded interactions are transferable, derived from the GROMOS force-field, apart for dihedrals that have been newly derived. Default C12 LJ parameters were also modified starting from the GROMOS one . The non-transferable (structure-based) part of the force field is obtained from state-of-the-art simulation(s) of the system of interest, sampling the fluctuations of specific states (i.e. monomer, apo, fibril, etc.), and is then weighted with a prior (or reference) simulation, obtained with a simplified description of the system. From the training and the reference simulations, we extract pair-wise distances and contact probabilities 𝑃 !" #$ and 𝑃 !" %& , respectively. Then, by Bayesian reweighting, an estimation of the atom-pairs non-bonded interaction energy as: