Nonlinear conjugate gradient (NCG) methods can generate search directions using only first-order information and a few dot products, making them attractive algorithms for solving large-scale optimization problems. However, even the most modern NCG methods can require large numbers of iterations and, therefore, many function evaluations to converge to a solution. This poses a challenge for simulation-constrained problems where the function evaluation entails expensive partial or ordinary differential equation solutions. Preconditioning can accelerate convergence and help compute a solution in fewer function evaluations. However, general-purpose preconditioners for nonlinear problems are challenging to construct. In this paper, we review a selection of classical and modern NCG methods, introduce their preconditioned variants, and propose a preconditioner based on the diagonalization of the BFGS formula. As with the NCG methods, this preconditioner utilizes only first-order information and requires only a small number of dot products. Our numerical experiments using CUTEst problems indicate that the proposed preconditioner successfully reduces the number of function evaluations at negligible additional cost for its update and application.