Active machine learning determines the optimal query for the either optimization or modeling purposes. However, for certain problems it is not evident that these queries are executable. For instance in catalyst or molecule design, novel compounds are proposed to synthesize and test on the property of interest. Here, the representation of the catalyst or molecule is crucial for the synthesizability of the queries. Synthesizability is defined as the feasibility of the proposed queries, referring to whether the proposed catalysts or molecules can be synthesized. Often, a simple representation of a catalyst is a vector containing the catalyst composition . This guarantees the synthesizability of the catalyst but limits the design space explored by the active machine learning algorithm as only the composition is varied but no structural or geometrical properties are considered. Ideally, one considers the complete catalyst space for every problem by for example considering the complete 3D-geometry as a representation for the catalyst site or molecule. However, not every imaginable catalyst or molecule 3D-geometry can be synthesizable, which makes that there is trade-off between the magnitude of the design space, so-called creativity, and synthesizability.