The demand for precise, data-efficient, and cost-effective exploration of chemical space has ignited growing interest in machine learning (ML), which exhibits remarkable capabilities in accelerating atomistic simulations of large systems over long time scales. Active learning is a technique widely used to reduce the cost of acquiring relevant ML training data. Here we present a modular, transferrable, and broadly applicable, parallel active learning orchestrator. Our workflow enables data and task parallelism for data generation, model training, and ML-enhanced simulations. We demonstrate its use in efficiently exploring multiple excited state potential energy surfaces and possible degradation pathways of an organic semiconductor used in organic light-emitting diodes. With our modular and adaptable workflow architecture, we expect our parallel active learning approach to be readily extended to explore other materials using state-of-the-art ML models, opening ways to AI-guided design and a better understanding of molecules and materials relevant to various applications, such as organic semiconductors or photocatalysts.