A new approach is demonstrated to approximate computational fluid dynamics (CFD) in urban tall building design contexts with complex wind interference. This is achieved by training an artificial neural network (ANN) on local shape and fluid features to return surface pressure on test model meshes of complex forms. This is as opposed to the use of global model parameters and Interference Factors (IF) commonly found in previous work. The ANN is trained using shape and fluid features extracted from a set of evaluated principal (design) models (PMs). The regression function is then used to predict results based on shape features from the PM and fluid features from a one-off obstruction model (OM), context only, simulation. For the application of early-stage generative design, the errors (against CFD validation) are less than 10% centred standard deviation s, whilst the front-end prediction times for the test cases are around 20s (up to 500 times faster than the CFD).