The Minimal Description Length (MDL) principle is a powerful and well founded approach, which has been successfully applied in a wide range of Data Mining tasks. In this paper we address the problem of pattern mining with MDL. We discuss how constraints-background knowledge on interestingness of patterns-can be embedded into MDL and argue the benefits of MDL over a simple selection of patterns based on measures.