A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these properties requires knowledge of crystal structures, which occur in three subtle variations (Heusler, inverse Heusler, and CsCl-type structures) that are difficult, and at times impossible, to distinguish by diffraction techniques. Compared to alternative approaches, this Heusler discovery engine performs exceptionally well, making fast and reliable predictions of the occurrence of Heusler vs non-Heusler compounds for an arbitrary combination of elements with no structural input on over 400 000 candidates. The model has a true positive rate of 0.94 (and false positive rate of 0.01). It is also valuable for data sanitizing, by flagging questionable entries in crystallographic databases. It was applied to screen candidates with the formula AB$\mathrm{<mrow\; data-mjx-texclass="ORD">2</mrow>}$C and predict the existence of 12 novel gallides MRu$\mathrm{<mrow\; data-mjx-texclass="ORD">2</mrow>}$Ga and RuM$\mathrm{<mrow\; data-mjx-texclass="ORD">2</mrow>}$Ga (M = Ti-Co) as Heusler compounds, which were confirmed experimentally. One member, TiRu$\mathrm{<mrow\; data-mjx-texclass="ORD">2</mrow>}$Ga, exhibited diagnostic superstructure peaks that confirm the adoption of an ordered Heusler as opposed to a disordered CsCl-type structure.