Land-use change models are often used to explore future land-use. Currently, most land-use change models run a small number of predetermined scenarios. To better address the multidimensional nature of uncertainty about the future, previous studies have argued for covering a wider range of the uncertainty space than is possible with existing scenario approaches. One way to approach this, is by using exploratory modelling. Instead of running the model on a small number of pre-defined scenarios, one uses sampling techniques over a plausible range of the uncertainty space to generate large-scale simulation experiments. With respect to land-use change models, this results in a large number of future land-use maps. To better make sense of the resulting maps, the next step necessitates the identification of plausible distinctive land-use patterns through clustering algorithms. Previous studies regarding quantification of land-use maps (dis)similarity focus only on comparing maps on a one-to-one basis or in small numbers for validation and calibration purposes. In this study the use of different map of similarity metrics on the resulting clusters of land-use patterns is systematically investigated. Specifically, the implications of using various cell-by-cell similarity metrics and landscape structure similarity metrics to cluster the resulting land-use maps are tested. The Land Use Scanner model is used for this purpose. It was found that the choice of (dis)similarity metric plays a significant role in the formed clusters of maps. If exploratory modelling is to be applied to land-use change models, it is thus important that great care is taken in the selection of the proper clustering algorithm.