High Entropy Alloys (HEA) are promising candidates as container materials in future Molten Salt Reactors (MSR) due to their good mechanical properties, irradiation tolerance and corrosion resistance. However, the straightforward evaluation of the most eligible alloys remains a challenging task due to the many variables involved (such as exposure time and salt composition). The objective of the current study is to present a strategy which overcomes this impediment and accelerates the design and discovery of encouraging HEA by means of artificial intelligence approaches. Firstly, a database is built upon an extensive literature survey where groups of commercial and homemade alloys were tested simultaneously at identical experimental conditions. Secondly, their overall performance is ranked with a pairwise comparison algorithm. Then, the assigned score is fitted as a function of chemical composition by a Gaussian process regression. Lastly, a multi-objective optimization algorithm is applied to obtain the best compromise between corrosion resistance, configurational entropy and microstructural constitution (evaluated by computational thermodynamics). Consequently, it is possible to identify the most superior HEA based on their chemical content and their desired properties. Some promising candidates will be selected for future microstructural characterization (X-ray diffraction, SEM, EBSD, EDS), for harness tests and molten salt corrosion experiments in chloride-based molten salts.