Optimizing a (preference) function through a small number of pairwise comparisons is challenging since pairwise comparisons provide limited information about the underlying function. In practice, preference functions often have a single peak, and this property could be exploited to speed up the optimization process. In this paper we describe a Bayesian optimization method aimed at achieving this.