Over the past decade there has been a shift away from the traditional static method of performing high throughput screening (HTS) against large chemical libraries where experiments are designed in advance, executed and then the generated data is processed. Traditionally, this results in additional rounds of biological validation, testing and lead compound follow up for medicinal chemistry. More recently there has been a movement to focus instead on an increased number of targeted chemical libraries and smaller initial HTS experiments which can be run in a more dynamic fashion with the resultant data processed automatically and in near real time to initiate new biological experimentation and even automated chemical synthesis on the fly. To make this possible it is necessary to have an underlying software and messaging infrastructure that can connect informatics platforms that utilize Artificial Intelligence and Machine Learning techniques to design experiments that can then be transferred to physical systems to initiate new experiments or small molecule synthesis. NCATS has developed such a platform with the initial validation being used to perform dynamic assay optimization but which is extensible to far more complex experimentation types to move beyond automation and instead towards autonomy.