The latest developments in artificial intelligence (AI) have arrived into an existing state of creative tension between computational and medicinal chemists. At their most productive, medicinal and computational chemists have made significant progress in delivering new therapeutic agents into the clinic. However, the relationship between these communities has the prospect of being weakened by application of oversimplistic AI methods that, if they fail to deliver, will reinforce unproductive prejudices. We review what can be learned from our history of integrating QSAR and structure-based methods into drug discovery. Now with synthesis and testing available as contract services, the environment for computational innovation has changed and we consider the impact this may have on the relationships in our disciplines. We discuss the current state of interdisciplinary communication and suggest approaches to bring the subdisciplines together in order to improve computational medicinal chemistry and, most importantly, deliver better medicines to the clinic faster.