Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIP) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, reactive MLIPs require refitting to extensive datasets for each new application, and prior knowledge of reaction networks is required to generate fitting data. In this work, we develop a general reactive MLIP through unbiased active learning with a nanoreactor molecular dynamics inspired sampler. The resulting potential (ANI-nr) is then applied to study five distinct condensed phase reactive chemistry problems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-nr closely matches experiment and/or previous studies using traditional model chemistry methods, without needing to be refit for each application, which enables high-throughput in silico reactive chemistry experimentation.