The paper introduces a new descriptor called the Disordered Enthalpy–Entropy Descriptor (DEED) to improve the discovery of high-entropy ceramics. DEED captures the balance between entropy gains and enthalpy costs, enabling the classification of functional synthesizability of multicomponent ceramics. The descriptor is designed to be computationally efficient, using a convolutional algorithm that reduces the number of configurations required for complex solid solutions. The authors validate DEED through experimental synthesis of new single-phase high-entropy carbonitrides and borides, demonstrating its accuracy and reliability. DEED also provides insights into microstructure formation in multiphase systems, making it a valuable tool for predicting the functional synthesizability of high-entropy ceramics. The method is integrated into the AFLOW computational ecosystem, offering a comprehensive approach to discovering novel high-entropy materials.The paper introduces a new descriptor called the Disordered Enthalpy–Entropy Descriptor (DEED) to improve the discovery of high-entropy ceramics. DEED captures the balance between entropy gains and enthalpy costs, enabling the classification of functional synthesizability of multicomponent ceramics. The descriptor is designed to be computationally efficient, using a convolutional algorithm that reduces the number of configurations required for complex solid solutions. The authors validate DEED through experimental synthesis of new single-phase high-entropy carbonitrides and borides, demonstrating its accuracy and reliability. DEED also provides insights into microstructure formation in multiphase systems, making it a valuable tool for predicting the functional synthesizability of high-entropy ceramics. The method is integrated into the AFLOW computational ecosystem, offering a comprehensive approach to discovering novel high-entropy materials.