We demonstrate the use of CL in REINVENT by formulating a complex task to design 3-phosphoinositidedependent protein kinase-1 (PDK1) inhibitors, adopting a structure-based optimization approach. We show that immediate states of productivity can be achieved by assembling a curriculum compared to standard policy-based RL, which can circumvent high computational costs associated with reward components such as molecular docking. Moreover, we show that CL provides a natural method for agent policy regularization, such that minor changes in the curriculum can steer molecular design, enabling control over the quality and diversity of the results in a predictable manner and leading to high quality molecules proposed for synthesis.