Inverse molecular design proposes molecules with predefined properties and is a reverse process of traditional direct design processes. In the reverse design process, molecules are optimized for the properties. One approach is to explore chemical space, a set of candidate molecules, and functionality space mapped onto the chemical space. In contrast to early efforts toward inverse design, exploration leads directly to chemical structures. Only the subspace of the chemical space is explored by using the information of the functionality space, and the exhaustive enumeration of the properties of all the candidate molecules is avoided. This efficiency is appealing because the chemical space combinatorially expands with molecular size. Thus, inverse design is a promising approach for accelerating the development of functional molecules and materials. Importantly, the choice of representation of molecules and their properties dramatically impacts the performance of molecular design. Electronic structure theories can map molecular structures onto various physical properties without experimental records and are a natural choice for inverse design. The inverse molecular design approaches based on electronic structure theories involve the variational particle approach, linear combination of atomic potentials (LCAP), and quantum algorithm-based alchemical optimization. Considering the geometric stability of molecules in the design process is essential for designing functional molecules available in experiments under ambient conditions. Recently, we proposed an inverse molecular design method based on quantum alchemy to efficiently explore chemical space composed of molecules with equilibrium geometries. Hereafter, this method is referred to as MDM. Quantum alchemy models the change in composition ("alchemical" changes) from a reference molecule to a target molecule at the level of quantum mechanics. MDM simultaneously predicts the molecular species, target properties, and equilibrium geometry of the functional molecule without empirical data. In the design process, MDM gradually varies the molecular species and equilibrium geometry using the gradient of the target property with respect to the molecular species that accounts for the change in equilibrium structure. MDM was applied to various chemical spaces, one of which contains 3.1 × 10 5 BN-doped phenanthrene derivatives, and its capabilities in the design of energy-related functionalities were demonstrated. MDM can target various molecular properties calculated by the quantum alchemy method. However, evaluating the property gradient other than for energy-related properties (e.g., energy, atomization energy, and reaction energy) is computationally expensive. This greatly limits the scope of applications. To overcome this problem, MDM applicable to the general properties with a small computational cost is needed.