Remote manipulation tasks in the small scale can often not be performed autonomously, due to the unstructured nature of the environments and the limited capabilities of sensor and localization technologies. For these tasks, teleoperated systems are used, in which the human operator is integral part of the control. In time-delayed teleoperation, the operator gradually adopts discrete control strategies, such as 'move-and-wait'. In this paper, we present and compare three different control strategies for driving a nanomanipulation system with direct vision feedback. Two strategies are based on a fixed step size to move the manipulator, while the third uses a variable step size. The strategies are compared on a 2D fine positioning task. Experimental results are in agreement with Fitts' law and show that the third strategy, besides allowing movements of size ranging across several orders of magnitude, also allows to complete the fine positioning task in less time. The control strategies can be used in general to control vision-guided teleoperation systems affected by time delay.