Euclid Quick Data Release (Q1). The strong-lensing discovery engine B. Early strong-lens candidates from the visual inspection of galaxies with a high-velocity dispersion

We present a search for strong gravitational lenses in Euclid imaging with a high stellar velocity dispersion (sigmav>180,̨ms) 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.