Here, we focus on several aspects for improving structure sampling via MD simulations during protein model refinement. First, we systematically benchmarked different restraining protocols. In particular, we focused on analyzing the application of restraints with flat-bottom harmonic function that was introduced by us earlier. Because of its functional form, flat-bottom restraints do not limit sampling within some range, while at same time still preventing large deviations from the initial model such as significant unfolding. Second, we explored the application of higher temperatures during the MD simulations. Elevated temperatures are expected to accelerate sampling such as in temperature replica exchange simulations , but increased temperatures can also cause thermal denaturation or alter the free energy landscape compared to physiological conditions in other ways. We tested here to what extent increased temperatures can be beneficial during structure refinement, especially when combined with restraints. Third, we tested an idea of enhancing sampling by initiating simulations from an augmented set of similar initial models. We hypothesized that refinement may be easier from some models than others and therefore refinement may be overall more successful than if only a single model is used, even if it is the best initial model in terms of distance from the true native structures. More specifically, we built alternative models starting from the initial model using additional template-based information for targets where this is possible. Simply combining simulations from multiple initial models also allowed a broader exploration of different parts of conformational space that may otherwise be separated by high kinetic barriers. Based on the insights from the detailed analysis of different aspects of our MD-based refinement method, we are presenting a new refinement protocol that leads to improved prediction accuracies based on our benchmark tests.