The discovery of new materials has been a driving force for scientific and technological progress. Recently, there has been a clear trend of involving various artificial intelligence-and machine learning-based technologies to accelerate this process on different levels. There are successful examples that apply a combination of computational techniques narrowed by domainspecific knowledge. AI and machine learning have been implemented in the discovery process, with arising challenges of implementing a completely human-free experimental workflow identified. For example, the presence of structural disorder (often only partially resolved) and the accommodation of continuously varying compositions in solid solutions is challenging to treat computationally. The augmentation of traditional exploratory synthesis methods with techniques and tools created to facilitate exploration has proved to be a successful approach for many different classes of materials, e.g., intermetallics or electrode materials. In the case of solid-state systems, exploration can be also hindered by the need for extreme or highly specific experimental conditions (high temperature or pressure, long reaction times etc.). A new material may form at conditions that differ from the typical norm for proximal phases or common structures, and despite the advances in prediction of potential synthesizability of theoretical materials researchers in practice often have to screen multiple synthetic pathways. There have been attempts to develop high-throughput synthetic techniques applicable to solid-state reactions; however, they cannot be applied for all systems given differences in solubility and reactivity within different solids. Another challenge arises from the higher dimensionality of unexplored chemical space as, due to multiple competing reactions in the multicomponent systems, some intermediate phases might be left undiscovered. There have been developments of in situ studies of phase formation in different systems, but such studies are generally unavailable once the reaction temperature becomes too high. Therefore, investigation of a solid-state system may require a combination of different exploration approaches in order to maximize the success rate. The discovery of new phases in high-dimensional chemical spaces is time-consuming and new tools and appropriate workflows that combine and thus build on existing tools are required to accelerate this process.