We investigate how weakly supervised methods like CWoLa and CATHODE can be used to enhance the sensitivity of searches at the LHC. These methods do not rely on truth level labels and are thus applicable in a model agnostic setting. In particular, we examine how these methods generalize to low level features, i.e. to higher dimensional inputs. As one example, we show how CWoLa can enhance the sensitivity of a monojet search at the LHC for models with modified jet dynamics.