We analyze the performance of different limited-memory quasi-Newton methods for unconstrained DNS-based optimization. Optimization based on Direct Numerical Simulation (DNS) of turbulent flows is extremely expensive, as functional and gradient evaluations require the simulation of Navier–Stokes and adjoint Navier–Stokes equations with high space and time resolution. Nowadays, simple and robust nonlinear conjugate gradient methods are generally used for DNS-based optimal control, as they do not require much memory overhead in a large control space. In the current study, we investigate the use of quasi-Newton methods instead. They combine a cheap approximation of the Hessian to improve step direction and step length, leading to faster convergence of the optimization. Since control spaces are often large in DNS-based optimization, we investigate only limited-memory quasi-Newton methods. Three methods are studied, i.e., the discrete truncated Newton method, the limited-memory BFGS method, and the damped L-BFGS method. The latter method is designed for constrained optimization, but can also address unconstrained problems. Furthermore, the damped L-BFGS method only requires the Armijo condition in the line search, not the Wolfe conditions, limiting expensive functional and gradient evaluations. We investigate the combination of the three quasi-Newton methods with three different line-search methods either based on bisection, quadratic interpolation, or cubic interpolation. Initially, all possible combinations are evaluated in a test problem that is based on the extended Rosenbrock function. The three best performing methods are further tested in two different DNS-based optimal control cases in a turbulent channel flow at Reτ=180. This reveals that the damped L-BFGS method in combination with a cubic line search performs best, closely followed by classical L-BFGS with cubic line search. Though the damped L-BFGS often requires a few more iterations to reach convergence, this is compensated by a more cost effective line search, with fewer functional and gradient evaluations. Moreover, compared to the conjugate-gradient method, damped L-BFGS speeds up convergence by a factor of four.