With hit identification and lead optimisation being key steps in the development of any new drug, the continued advancements in machine learning and artificial intelligence approaches possess significant promise to streamline this process, which would result in more efficient medicinal chemistry campaigns. In the absence of target structural information, a crowdsourced approach was used to develop predictive models for a promising antimalarial series. Importantly, the winning models of the most recent competition round were used to generate novel compounds, which were then synthesised and evaluated for experimental validation of each model leading to a new counterintuitive "active". The simple open science and crowdsourcing principles used throughout this campaign are applicable to many medicinal chemistry projects, whereby the community's combined efforts can be used to accelerate the early stages of drug discovery and involve participants from public and private sectors. The work conducted here has been designed to be "living", in that all methods and results are publicly available and contributions can continue to be made by anyone because everyone has access to all data and ideas.