This paper introduces the $f$-EI$(φ)$ algorithm, a novel iterative algorithm which operates on measures and performs $f$-divergence minimisation in a Bayesian framework. We prove that for a rich family of values of $(f,φ)$ this algorithm leads at each step to a systematic decrease in the $f$-divergence and show that we achieve an optimum. In the particular case where we consider a weighted sum of Dirac measures and the $α$-divergence, we obtain that the calculations involved in the $f$-EI$(φ)$ algorithm simplify to gradient-based computations. Empirical results support the claim that the $f$-EI$(φ)$ algorithm serves as a powerful tool to assist Variational methods.