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Compositionally complex solid solution electrocatalysts span vast composition spaces, and even one materials system can contain more candidate compositions than can be measured exhaustively. Here, we compare how five text-derived composition representations influence the outcome of a common label-free filtering task. A focused-corpus Word2Vec model is compared with MatSciBERT and Qwen embeddings, with multicomponent compositions encoded either by concentration-weighted element vectors or by full-composition prompts. Similarities to the concepts conductivity and dielectric define a 2D semantic space in which the same symmetric Pareto-front selection rule is applied to every representation. Across 14 combinatorial HER, ORR, and OER materials libraries, the representations produce distinct trade-offs between candidate reduction and best-current retention. MatSciBERT\_Full gives the smallest subsets on average, whereas Word2Vec gives the smallest mean best-current deviation while retaining comparably compact subsets. These differences show that representation construction changes the behavior of the same downstream filter.