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Mastering the challenge of predicting properties of unknown materials with multiple principal elements (high entropy alloys/compositionally complex solid solutions) is crucial for the speedup in materials discovery. We show and discuss three models{,} using experimentally measured electrocatalytic performance data from two ternary systems (Ag–Pd–Ru; Ag–Pd–Pt){,} to predict electrocatalytic performance in the shared quaternary system (Ag–Pd–Pt–Ru). As a starting point{,} we apply Gaussian Process Regression (GPR) based on composition as the feature{,} which includes both Ag and Pd{,} achieving an initial correlation coefficient for the prediction (r) of 0.63 and a determination coefficient (r2) of 0.08. Second{,} we present a version of the GPR model using word embedding-derived materials vectors as features. Using materials-specific embedding vectors significantly improves the predictions{,} evident from an improved r2 of 0.65. The third model is based on a ‘standard vector method’ which synthesizes weighted vector representations of material properties as features{,} then creating a reference vector that results in a very good correlation with the quaternary system{'}s material performance (resulting r of 0.94). Our approach demonstrates that existing experimental data combined with the latent knowledge of word embedding-derived representations of materials can be used effectively for materials discovery where data is typically scarce