Given any (forward) Brascamp-Lieb inequality on euclidean space, a famous theorem of Lieb guarantees that gaussian near-maximizers always exist. Recently, Barthe and Wolff used mass transportation techniques to establish a counterpart to Lieb's theorem for all non-degenerate cases of the inverse Brascamp-Lieb inequality. Here we build on work of Chen-Dafnis-Paouris and employ heat-flow techniques to understand the inverse Brascamp-Lieb inequality for certain regularized input functions, in particular extending the Barthe-Wolff theorem to such a setting. Inspiration arose from work of Bennett, Carbery, Christ and Tao for the forward inequality, as well as Wolff's surprising observation that the gaussian saturation property for the inverse Brascamp-Lieb inequality implies the gaussian saturation property for both the forward Brascamp-Lieb inequality and Barthe's reverse Brascamp-Lieb inequality.