We would love to hear more information (sample size, etc.). How many of the variables selected by computer (in your perfectly fitted model) was entered into the model with the adjusted r-squared of 0.4? Furthermore, note that the forward selection method does not select the significant variables to enter the next model. It has some criteria, but not exactly what you manually did to replicate what computer does. So, if you enter the same independent variables in the last model of the forward selection method into your manually selected model, the result will be the same as the computerized forward selection method (again an adjusted r-squared of about 1.0).

The adjusted r-squared indicates how your model fits the data well, so when you enter more variables, it is possible that your model becomes better as a whole, and the adjusted r-squared increases. However, when you prune some of the variables that were non-significant, your model which depended on them too, becomes weaker in explaining your experiment, and thus your adjusted r-squared decreases. So it is possible to have an adjusted r-squared value close to 1, while the model is very complicated and actually of little or no practical use (despite being very accurate).