Rojas, KarinaCollett, ThomasKaragiannis, Dionysios2026-09-112026-09-112026Rojas, K., Collett, T.E., Barroso, J.A., Nightingale, J.W., Stern, D., Moustakas, L.A., Schuldt, S., Despali, G., Melo, A., Walmsley, M. and Ballard, D.J., 2025. Euclid Quick Data Release (Q1) The Strong Lensing Discovery Engine B--Early strong lens candidates from visual inspection of high velocity dispersion galaxies. arXiv preprint arXiv:2503.15325.https://doi.org/10.1051/0004-6361/202554605https://hdl.handle.net/10566/25414We present a search for strong gravitational lenses in Euclid imaging with a high stellar velocity dispersion (σv > 180 km s−1) reported by SDSS and DESI. We performed expert visual inspection and classification of 11 660 Euclid images. We discovered 38 grade A and 40 grade B candidate lenses, which is consistent with an expected sample of ∼32. Palomar spectroscopy confirmed 5 lens systems, while DESI spectra confirmed one system, provided ambiguous results for another, and helped to discard a third system. The Euclid automated lens modeler modelled 53 candidates, confirmed 38 as lenses, failed to model 9, and ruled out 6 grade B candidates. For the remaining 25 candidates, we were unable to gather additional information. More importantly, our classified non-lenses provide an excellent training set for machine-learning lens classifiers. We created high-fidelity simulations of Euclid lenses by painting realistic lensed sources behind the tagged (non-lens) luminous red galaxies. This training set is the foundation stone for the Euclid galaxy-galaxy strong-lensing discovery engine.encatalogsgravitational lensing: strongmethods: statisticalDispersionsEnginesArticle