Motion Matching is a simple yet powerful way of animating characters in games. Compared to other methods, it doesn’t require very much manual work once you have a basic set-up: there is no need to structure clips in graphs, to carefully cut or synchronize them, or to explicitly create new transitions between states. However, Motion Matching works best when combined with lots of motion capture data, and all of that data comes at a cost: large memory usage, which only gets worse the more the system grows, and the more situations it is applied in. Here we present a solution to this problem called Learned Motion Matching which leverages Machine Learning to drastically reduce the memory usage of Motion Matching based animation systems.