Associative memories (AM) deliver instant recall and tolerate noisy inputs, yet their effectiveness hinges on how numeric data are translated into bits. This study benchmarks five integer-to-binary schemes with different approaches, code—within the classic Lernmatrix associative memory. Fourteen public datasets with varied sizes, feature counts, and class balances were converted to non-negative integers, harmonized to a common bit-width, binarized, and validated by Leave-One-Out cross-validation. Balanced-accuracy results show that Parity + RNS (mean BA = 0.6826) and Gray code (0.6788) provide the most informative representations, leading eight of the fourteen datasets. Straight Binary and BCD excel only in niche cases, whereas IEEE-754 mantissa remains the least effective (0.6457). The distributed residues of Parity + RNS and the one-bit transitions of Gray code make class boundaries easier to separate, boosting accuracy by more than five percentage points over less suitable encodings. These results demonstrate that careful bit-level design can markedly improve one-shot associative classifiers, underscoring the importance of representation as an independent lever for optimizing lightweight, interpretable learning systems.