Predictive Control optimization typically relies on exhaustive enumeration of all possible voltage vectors within the finite control set. For simpler implementation the computational burden is not a concern, but the limited control set can demand a very high sampling frequency to reach good steady state performance. Indirect Predictive Control or Modulated Predictive Control addresses this by using a modulation stage, but classic implementation depends on duty cycle calculation that can limit constraints and non linearities in the cost function. This paper introduces the Informed Binary Search, an intelligent search algorithm capable of handling any constraints within the cost function, similarly to Finite Control Set, but with the benefit of fixed switching frequency. Experimental results on a two-level VSI driving an induction motor is shown but the method can be applied to every setup of machine and inverter. The results are compared with PTC, PTC with DSVM and MPTC and the metrics show improvements in torque and flux ripple and improvements of current THD up to 30% over classic M2PC while having similar computational burden.