The problem of finding score-maximizing Bayesian Networks, where the score represents quality of fit to input data, can be modeled as an integer program, and some of the state-of-the-art algorithms for this problem indeed solve such integer programs. We discuss recent work on integer programming models for important variants of the Bayesian Network Structure Learning problem.