The gravitational-wave (GW) detections reported by the LIGO–Virgo–KAGRA (LVK) Collaboration have so far been consistent with quasi-circular compact binary coalescences. However, a small fraction of binaries driven to merge through dynamical interactions in dense stellar environments or in field triples may retain measurable orbital eccentricity upon entering the sensitive frequency band of the LVK detectors. A confident measurement of eccentricity in the LVK band would constitute decisive evidence for such dynamically driven mergers. Eccentric waveform models, however, are computationally expensive, so performing production-level inference on detected signals is an inefficient use of resources when eccentric signals are expected to be rare. In this work, we propose a parsimonious strategy that analyses the (WST) coefficients of GW signals as an intermediate step between and : each signal is assessed for the potential presence of eccentricity in order to provide rapid recommendations for which signals should undergo production-level eccentric inference. By compressing the signals and working with lower-dimensional classifiers, this approach reduces computational complexity and thereby speeds up classification. We evaluate the discriminatory power of the WST using a simple one-dimensional convolutional neural network trained on a large set of synthetic waveforms injected into realistic noise. We find that the WST representation enables effective discrimination between eccentric and quasi-circular binaries; we discuss the advantages of its compact, multi-scale representation and benchmark it against both simpler and more complex classifiers. Using an effective one-body waveform model, our pipeline achieves a detection accuracy of $\sim$$64\%$ at a false-alarm rate of $10\%$, with an AUC of $0.844$ and an average precision of $0.876$. Finally, we investigate the WST coefficients’ ability to distinguish eccentricity from spin-induced precession, and find that it performs well across a range of spin-precession magnitudes. We conclude that the WST is a powerful tool for identifying the fingerprints of eccentricity in compact binary coalescence GW signals.