We further improved refinement success by considering additional alternative initial models. The additional improvements over refinement based on a single model were quite significant, especially in terms of RMSD and SphereGrinder metrics. Presumably, the improvements from using multiple initial models are due to both alternative model generation and simply the extended sampling via MD simulations. Model quality improvements were tracked step by step (Figure ). The finding that refinement from additional models led to such significant differences may be surprising given that the CASP refinement targets are largely selected as one of the best initial predictions and most alternative initial models are likely worse in initial accuracy. However, we show that rather than overall initial accuracy, the key quantity for determining refinement success is that the local, per-residue accuracy surpasses a given threshold and it is therefore very possible that structures that are considered overall less accurate may actually be more refinable. Moreover, our ensemble selection and averaging protocol is well-equipped to recognize via scoring which initial models lead to the most refined structures so that it is not necessary to determine beforehand which initial model may be more refinable. As always, there is still room for improvement to achieve better model selection during refinement with multiple initial models since the best out of five model is yet significantly better than the model 1 that was selected (Table ). However, we note that for many applications such as the generation of initial models for solving crystal structures via molecular replacement, it may be sufficient to generate just one out of five models at very high accuracy even without knowing a priori which one is most accurate, as there may be other criteria such as experimental observables to distinguish between models.