Active machine learning is excellently suited for chemical engineering researchers to speed up experimental campaigns ranging from molecule and catalyst design, up to reaction and reactor design. However, among experimental researchers active machine learning is less known and many active machine learning applications are not user-friendly today. A better collaboration between machine learning experts and chemical engineers can overcome these barriers. This interaction also helps to tune active machine learning algorithms depending on the applied (automated) experimental units and procedures, which improves the performance of these algorithms. To fully profit from the creativity of active machine learning, improvements on the machine learning methods related to data transfer and synthesizability are still required. By harmonizing synthesizability and creativity, active machine learning is bound to make significant advances in the fields of molecule and catalyst synthesis. The recent promising breakthroughs will allow active machine learning to become an essential tool for the chemical engineer and further facilitate autonomous and efficient scientific discoveries which will contribute to a more sustainable chemical industry in the future.