Calibration of parameters in simulation models is necessary to develop sharp predictions with quantified uncertainty. A scalable method for calibration involves building an emulator after conducting an experiment on the simulation model. However, when the parameter space is large, meaning the parameters are quite uncertain prior to calibration, much of the parameter space can produce unstable or unrealistic simulator responses that drastically differ from the observed data. One solution to this problem is to simply discard, or filter out, the parameters that gave unreasonable responses and then build an emulator only on the remaining simulator responses. In this article, we demonstrate the key mechanics for an approach that emulates filtered responses but also avoids unstable and incorrect inference. These ideas are illustrated on a real data example of calibrating COVID-19 epidemiological simulation model.