For estimating the density ofY|μ ∼Nd(μ, νId) based onX|μ ∼Nd(μ, σ2XId) with known ν, σ2X, we consider the classPof “extended plug-in” predictive densitiesq∼Nd(μ, νId). For a given prior density π for μ and Kullback-Leibler loss, we investigate the optimal choiceqeb,π obtained by minimizing the expected posterior loss amongq∈P, as initially proposed by Okudo and Komaki [21]. Withqeb,π having a simple form and a appealing alternative to the exact Bayesian predictive density, we investigate its Kullback-Leibler risk performance. Our main finding consists, ford≥ 3 and a given superharmonic prior density π, in the determination of a lower cut-off point ν such thatqeb,π dominates the benchmark minimum risk and minimax predictive density for ν ≥ ν. Specific analyses are carried out and our results are illustrated for a pseudo-Bayes marginal density and a subclass of Strawderman prior densities.