In silico neural responses from encoding models increasingly resemble in vivo responses recorded from real brains, enabling the novel research paradigm of in silico neuroscience. In silico neural responses are quick and cheap to generate, allowing researchers to explore and test scientific hypotheses across vastly larger solution spaces than possible in vivo. Novel findings from large-scale in silico experimentation are then validated through targeted small-scale in vivo data collection, in this way optimizing research resources. Thus, in silico neuroscience scales beyond what is possible with in vivo data, and democratizes research across groups with diverse data collection infrastructure and resources. To promote this emerging research paradigm, here we release the Neural Encoding Simulation Toolkit (NEST). NEST consists of trained encoding models of the brain, accompanied with a Python package, to facilitate the generation of in silico neural responses to arbitrary stimuli. If you wish to contribute to improving and expanding NEST (e.g., with more accurate encoding models, or new neural datasets), then please get in touch with Ale (alessandro.gifford@gmail.com): all feedback and help is strongly appreciated!