In this paper, we study the problem of noisy, convex, zeroth-order optimisation of a function f over a bounded convex set \overline{\mathcal{X}}\subset \mathbb{R}^{d} . Given a budget n of noisy queries to the function f that can be allocated sequentially and adaptively, our aim is to construct an algorithm that returns a point \hat x\in \overline{\mathcal{X}} such that f(\hat x) is as small as possible. We provide a conceptually simple method inspired by the textbook centre of gravity method, but adapted to the noisy and zeroth-order setting. We prove that this method is such that the f(\hat x) - \min_{x\in \overline{\mathcal{X}}}f(x) is of smaller order than d^{2}/\sqrt{n} up to poly-logarithmic terms. We slightly improve upon literature preceding this work, where the best-known rate was in Lattimore (2019) and was of order d^{2.5}/\sqrt{n} , albeit for a more challenging problem – yet in the literature contemporaneous to our work, the remarkable work of Fokkema et al. (2024) attains the faster rate of d^{1.5}/\sqrt{n} under mild conditions on \overline{\mathcal{X}} . Our main contribution is, however, conceptual, as we believe that our algorithm and its analysis bring novel ideas and are significantly simpler than the existing approaches.